presenting a research article

Princeton Correspondents on Undergraduate Research

How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

• Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

• Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

• Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

• Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

• Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

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How to make a scientific presentation

Scientific presentation outlines

Questions to ask yourself before you write your talk, 1. how much time do you have, 2. who will you speak to, 3. what do you want the audience to learn from your talk, step 1: outline your presentation, step 2: plan your presentation slides, step 3: make the presentation slides, slide design, text elements, animations and transitions, step 4: practice your presentation, final thoughts, frequently asked questions about preparing scientific presentations, related articles.

A good scientific presentation achieves three things: you communicate the science clearly, your research leaves a lasting impression on your audience, and you enhance your reputation as a scientist.

But, what is the best way to prepare for a scientific presentation? How do you start writing a talk? What details do you include, and what do you leave out?

It’s tempting to launch into making lots of slides. But, starting with the slides can mean you neglect the narrative of your presentation, resulting in an overly detailed, boring talk.

The key to making an engaging scientific presentation is to prepare the narrative of your talk before beginning to construct your presentation slides. Planning your talk will ensure that you tell a clear, compelling scientific story that will engage the audience.

In this guide, you’ll find everything you need to know to make a good oral scientific presentation, including:

• The different types of oral scientific presentations and how they are delivered;
• How to outline a scientific presentation;
• How to make slides for a scientific presentation.

Our advice results from delving into the literature on writing scientific talks and from our own experiences as scientists in giving and listening to presentations. We provide tips and best practices for giving scientific talks in a separate post.

There are two main types of scientific talks:

• Your talk focuses on a single study . Typically, you tell the story of a single scientific paper. This format is common for short talks at contributed sessions in conferences.
• Your talk describes multiple studies. You tell the story of multiple scientific papers. It is crucial to have a theme that unites the studies, for example, an overarching question or problem statement, with each study representing specific but different variations of the same theme. Typically, PhD defenses, invited seminars, lectures, or talks for a prospective employer (i.e., “job talks”) fall into this category.

➡️ Learn how to prepare an excellent thesis defense

The length of time you are allotted for your talk will determine whether you will discuss a single study or multiple studies, and which details to include in your story.

The background and interests of your audience will determine the narrative direction of your talk, and what devices you will use to get their attention. Will you be speaking to people specializing in your field, or will the audience also contain people from disciplines other than your own? To reach non-specialists, you will need to discuss the broader implications of your study outside your field.

The needs of the audience will also determine what technical details you will include, and the language you will use. For example, an undergraduate audience will have different needs than an audience of seasoned academics. Students will require a more comprehensive overview of background information and explanations of jargon but will need less technical methodological details.

Your goal is to speak to the majority. But, make your talk accessible to the least knowledgeable person in the room.

This is called the thesis statement, or simply the “take-home message”. Having listened to your talk, what message do you want the audience to take away from your presentation? Describe the main idea in one or two sentences. You want this theme to be present throughout your presentation. Again, the thesis statement will depend on the audience and the type of talk you are giving.

Your thesis statement will drive the narrative for your talk. By deciding the take-home message you want to convince the audience of as a result of listening to your talk, you decide how the story of your talk will flow and how you will navigate its twists and turns. The thesis statement tells you the results you need to show, which subsequently tells you the methods or studies you need to describe, which decides the angle you take in your introduction.

➡️ Learn how to write a thesis statement

The goal of your talk is that the audience leaves afterward with a clear understanding of the key take-away message of your research. To achieve that goal, you need to tell a coherent, logical story that conveys your thesis statement throughout the presentation. You can tell your story through careful preparation of your talk.

Preparation of a scientific presentation involves three separate stages: outlining the scientific narrative, preparing slides, and practicing your delivery. Making the slides of your talk without first planning what you are going to say is inefficient.

Here, we provide a 4 step guide to writing your scientific presentation:

• Outline your presentation
• Plan your presentation slides
• Make the presentation slides
• Practice your presentation

Writing an outline helps you consider the key pieces of your talk and how they fit together from the beginning, preventing you from forgetting any important details. It also means you avoid changing the order of your slides multiple times, saving you time.

Plan your talk as discrete sections. In the table below, we describe the sections for a single study talk vs. a talk discussing multiple studies:

The following tips apply when writing the outline of a single study talk. You can easily adapt this framework if you are writing a talk discussing multiple studies.

Introduction: Writing the introduction can be the hardest part of writing a talk. And when giving it, it’s the point where you might be at your most nervous. But preparing a good, concise introduction will settle your nerves.

The introduction tells the audience the story of why you studied your topic. A good introduction succinctly achieves four things, in the following order.

• It gives a broad perspective on the problem or topic for people in the audience who may be outside your discipline (i.e., it explains the big-picture problem motivating your study).
• It describes why you did the study, and why the audience should care.
• It gives a brief indication of how your study addressed the problem and provides the necessary background information that the audience needs to understand your work.
• It indicates what the audience will learn from the talk, and prepares them for what will come next.

A good introduction not only gives the big picture and motivations behind your study but also concisely sets the stage for what the audience will learn from the talk (e.g., the questions your work answers, and/or the hypotheses that your work tests). The end of the introduction will lead to a natural transition to the methods.

Give a broad perspective on the problem. The easiest way to start with the big picture is to think of a hook for the first slide of your presentation. A hook is an opening that gets the audience’s attention and gets them interested in your story. In science, this might take the form of a why, or a how question, or it could be a statement about a major problem or open question in your field. Other examples of hooks include quotes, short anecdotes, or interesting statistics.

Why should the audience care? Next, decide on the angle you are going to take on your hook that links to the thesis of your talk. In other words, you need to set the context, i.e., explain why the audience should care. For example, you may introduce an observation from nature, a pattern in experimental data, or a theory that you want to test. The audience must understand your motivations for the study.

Supplementary details. Once you have established the hook and angle, you need to include supplementary details to support them. For example, you might state your hypothesis. Then go into previous work and the current state of knowledge. Include citations of these studies. If you need to introduce some technical methodological details, theory, or jargon, do it here.

Conclude your introduction. The motivation for the work and background information should set the stage for the conclusion of the introduction, where you describe the goals of your study, and any hypotheses or predictions. Let the audience know what they are going to learn.

Methods: The audience will use your description of the methods to assess the approach you took in your study and to decide whether your findings are credible. Tell the story of your methods in chronological order. Use visuals to describe your methods as much as possible. If you have equations, make sure to take the time to explain them. Decide what methods to include and how you will show them. You need enough detail so that your audience will understand what you did and therefore can evaluate your approach, but avoid including superfluous details that do not support your main idea. You want to avoid the common mistake of including too much data, as the audience can read the paper(s) later.

Results: This is the evidence you present for your thesis. The audience will use the results to evaluate the support for your main idea. Choose the most important and interesting results—those that support your thesis. You don’t need to present all the results from your study (indeed, you most likely won’t have time to present them all). Break down complex results into digestible pieces, e.g., comparisons over multiple slides (more tips in the next section).

Summary: Summarize your main findings. Displaying your main findings through visuals can be effective. Emphasize the new contributions to scientific knowledge that your work makes.

Conclusion: Complete the circle by relating your conclusions to the big picture topic in your introduction—and your hook, if possible. It’s important to describe any alternative explanations for your findings. You might also speculate on future directions arising from your research. The slides that comprise your conclusion do not need to state “conclusion”. Rather, the concluding slide title should be a declarative sentence linking back to the big picture problem and your main idea.

It’s important to end well by planning a strong closure to your talk, after which you will thank the audience. Your closing statement should relate to your thesis, perhaps by stating it differently or memorably. Avoid ending awkwardly by memorizing your closing sentence.

By now, you have an outline of the story of your talk, which you can use to plan your slides. Your slides should complement and enhance what you will say. Use the following steps to prepare your slides.

• Write the slide titles to match your talk outline. These should be clear and informative declarative sentences that succinctly give the main idea of the slide (e.g., don’t use “Methods” as a slide title). Have one major idea per slide. In a YouTube talk on designing effective slides , researcher Michael Alley shows examples of instructive slide titles.
• Decide how you will convey the main idea of the slide (e.g., what figures, photographs, equations, statistics, references, or other elements you will need). The body of the slide should support the slide’s main idea.
• Under each slide title, outline what you want to say, in bullet points.

In sum, for each slide, prepare a title that summarizes its major idea, a list of visual elements, and a summary of the points you will make. Ensure each slide connects to your thesis. If it doesn’t, then you don’t need the slide.

Slides for scientific presentations have three major components: text (including labels and legends), graphics, and equations. Here, we give tips on how to present each of these components.

• Have an informative title slide. Include the names of all coauthors and their affiliations. Include an attractive image relating to your study.
• Make the foreground content of your slides “pop” by using an appropriate background. Slides that have white backgrounds with black text work well for small rooms, whereas slides with black backgrounds and white text are suitable for large rooms.
• The layout of your slides should be simple. Pay attention to how and where you lay the visual and text elements on each slide. It’s tempting to cram information, but you need lots of empty space. Retain space at the sides and bottom of your slides.
• Use sans serif fonts with a font size of at least 20 for text, and up to 40 for slide titles. Citations can be in 14 font and should be included at the bottom of the slide.
• Use bold or italics to emphasize words, not underlines or caps. Keep these effects to a minimum.
• Use concise text . You don’t need full sentences. Convey the essence of your message in as few words as possible. Write down what you’d like to say, and then shorten it for the slide. Remove unnecessary filler words.
• Text blocks should be limited to two lines. This will prevent you from crowding too much information on the slide.
• Include names of technical terms in your talk slides, especially if they are not familiar to everyone in the audience.
• Proofread your slides. Typos and grammatical errors are distracting for your audience.
• Include citations for the hypotheses or observations of other scientists.
• Good figures and graphics are essential to sustain audience interest. Use graphics and photographs to show the experiment or study system in action and to explain abstract concepts.
• Don’t use figures straight from your paper as they may be too detailed for your talk, and details like axes may be too small. Make new versions if necessary. Make them large enough to be visible from the back of the room.
• Use graphs to show your results, not tables. Tables are difficult for your audience to digest! If you must present a table, keep it simple.
• Label the axes of graphs and indicate the units. Label important components of graphics and photographs and include captions. Include sources for graphics that are not your own.
• Explain all the elements of a graph. This includes the axes, what the colors and markers mean, and patterns in the data.
• Use colors in figures and text in a meaningful, not random, way. For example, contrasting colors can be effective for pointing out comparisons and/or differences. Don’t use neon colors or pastels.
• Use thick lines in figures, and use color to create contrasts in the figures you present. Don’t use red/green or red/blue combinations, as color-blind audience members can’t distinguish between them.
• Arrows or circles can be effective for drawing attention to key details in graphs and equations. Add some text annotations along with them.
• Write your summary and conclusion slides using graphics, rather than showing a slide with a list of bullet points. Showing some of your results again can be helpful to remind the audience of your message.
• If your talk has equations, take time to explain them. Include text boxes to explain variables and mathematical terms, and put them under each term in the equation.
• Combine equations with a graphic that shows the scientific principle, or include a diagram of the mathematical model.
• Use animations judiciously. They are helpful to reveal complex ideas gradually, for example, if you need to make a comparison or contrast or to build a complicated argument or figure. For lists, reveal one bullet point at a time. New ideas appearing sequentially will help your audience follow your logic.
• Slide transitions should be simple. Silly ones distract from your message.
• Decide how you will make the transition as you move from one section of your talk to the next. For example, if you spend time talking through details, provide a summary afterward, especially in a long talk. Another common tactic is to have a “home slide” that you return to multiple times during the talk that reinforces your main idea or message. In her YouTube talk on designing effective scientific presentations , Stanford biologist Susan McConnell suggests using the approach of home slides to build a cohesive narrative.

To deliver a polished presentation, it is essential to practice it. Here are some tips.

• For your first run-through, practice alone. Pay attention to your narrative. Does your story flow naturally? Do you know how you will start and end? Are there any awkward transitions? Do animations help you tell your story? Do your slides help to convey what you are saying or are they missing components?
• Next, practice in front of your advisor, and/or your peers (e.g., your lab group). Ask someone to time your talk. Take note of their feedback and the questions that they ask you (you might be asked similar questions during your real talk).
• Edit your talk, taking into account the feedback you’ve received. Eliminate superfluous slides that don’t contribute to your takeaway message.
• Practice as many times as needed to memorize the order of your slides and the key transition points of your talk. However, don’t try to learn your talk word for word. Instead, memorize opening and closing statements, and sentences at key junctures in the presentation. Your presentation should resemble a serious but spontaneous conversation with the audience.
• Practicing multiple times also helps you hone the delivery of your talk. While rehearsing, pay attention to your vocal intonations and speed. Make sure to take pauses while you speak, and make eye contact with your imaginary audience.
• Make sure your talk finishes within the allotted time, and remember to leave time for questions. Conferences are particularly strict on run time.
• Anticipate questions and challenges from the audience, and clarify ambiguities within your slides and/or speech in response.
• If you anticipate that you could be asked questions about details but you don’t have time to include them, or they detract from the main message of your talk, you can prepare slides that address these questions and place them after the final slide of your talk.

➡️ More tips for giving scientific presentations

An organized presentation with a clear narrative will help you communicate your ideas effectively, which is essential for engaging your audience and conveying the importance of your work. Taking time to plan and outline your scientific presentation before writing the slides will help you manage your nerves and feel more confident during the presentation, which will improve your overall performance.

A good scientific presentation has an engaging scientific narrative with a memorable take-home message. It has clear, informative slides that enhance what the speaker says. You need to practice your talk many times to ensure you deliver a polished presentation.

First, consider who will attend your presentation, and what you want the audience to learn about your research. Tailor your content to their level of knowledge and interests. Second, create an outline for your presentation, including the key points you want to make and the evidence you will use to support those points. Finally, practice your presentation several times to ensure that it flows smoothly and that you are comfortable with the material.

Prepare an opening that immediately gets the audience’s attention. A common device is a why or a how question, or a statement of a major open problem in your field, but you could also start with a quote, interesting statistic, or case study from your field.

Scientific presentations typically either focus on a single study (e.g., a 15-minute conference presentation) or tell the story of multiple studies (e.g., a PhD defense or 50-minute conference keynote talk). For a single study talk, the structure follows the scientific paper format: Introduction, Methods, Results, Summary, and Conclusion, whereas the format of a talk discussing multiple studies is more complex, but a theme unifies the studies.

Ensure you have one major idea per slide, and convey that idea clearly (through images, equations, statistics, citations, video, etc.). The slide should include a title that summarizes the major point of the slide, should not contain too much text or too many graphics, and color should be used meaningfully.

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Open Access

Ten simple rules for effective presentation slides

* E-mail: [email protected]

Affiliation Biomedical Engineering and the Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America

• Kristen M. Naegle

Published: December 2, 2021

• https://doi.org/10.1371/journal.pcbi.1009554
• Reader Comments

Citation: Naegle KM (2021) Ten simple rules for effective presentation slides. PLoS Comput Biol 17(12): e1009554. https://doi.org/10.1371/journal.pcbi.1009554

Copyright: © 2021 Kristen M. Naegle. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The author received no specific funding for this work.

Competing interests: The author has declared no competing interests exist.

Introduction

The “presentation slide” is the building block of all academic presentations, whether they are journal clubs, thesis committee meetings, short conference talks, or hour-long seminars. A slide is a single page projected on a screen, usually built on the premise of a title, body, and figures or tables and includes both what is shown and what is spoken about that slide. Multiple slides are strung together to tell the larger story of the presentation. While there have been excellent 10 simple rules on giving entire presentations [ 1 , 2 ], there was an absence in the fine details of how to design a slide for optimal effect—such as the design elements that allow slides to convey meaningful information, to keep the audience engaged and informed, and to deliver the information intended and in the time frame allowed. As all research presentations seek to teach, effective slide design borrows from the same principles as effective teaching, including the consideration of cognitive processing your audience is relying on to organize, process, and retain information. This is written for anyone who needs to prepare slides from any length scale and for most purposes of conveying research to broad audiences. The rules are broken into 3 primary areas. Rules 1 to 5 are about optimizing the scope of each slide. Rules 6 to 8 are about principles around designing elements of the slide. Rules 9 to 10 are about preparing for your presentation, with the slides as the central focus of that preparation.

Rule 1: Include only one idea per slide

Each slide should have one central objective to deliver—the main idea or question [ 3 – 5 ]. Often, this means breaking complex ideas down into manageable pieces (see Fig 1 , where “background” information has been split into 2 key concepts). In another example, if you are presenting a complex computational approach in a large flow diagram, introduce it in smaller units, building it up until you finish with the entire diagram. The progressive buildup of complex information means that audiences are prepared to understand the whole picture, once you have dedicated time to each of the parts. You can accomplish the buildup of components in several ways—for example, using presentation software to cover/uncover information. Personally, I choose to create separate slides for each piece of information content I introduce—where the final slide has the entire diagram, and I use cropping or a cover on duplicated slides that come before to hide what I’m not yet ready to include. I use this method in order to ensure that each slide in my deck truly presents one specific idea (the new content) and the amount of the new information on that slide can be described in 1 minute (Rule 2), but it comes with the trade-off—a change to the format of one of the slides in the series often means changes to all slides.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

Top left: A background slide that describes the background material on a project from my lab. The slide was created using a PowerPoint Design Template, which had to be modified to increase default text sizes for this figure (i.e., the default text sizes are even worse than shown here). Bottom row: The 2 new slides that break up the content into 2 explicit ideas about the background, using a central graphic. In the first slide, the graphic is an explicit example of the SH2 domain of PI3-kinase interacting with a phosphorylation site (Y754) on the PDGFR to describe the important details of what an SH2 domain and phosphotyrosine ligand are and how they interact. I use that same graphic in the second slide to generalize all binding events and include redundant text to drive home the central message (a lot of possible interactions might occur in the human proteome, more than we can currently measure). Top right highlights which rules were used to move from the original slide to the new slide. Specific changes as highlighted by Rule 7 include increasing contrast by changing the background color, increasing font size, changing to sans serif fonts, and removing all capital text and underlining (using bold to draw attention). PDGFR, platelet-derived growth factor receptor.

https://doi.org/10.1371/journal.pcbi.1009554.g001

Rule 2: Spend only 1 minute per slide

When you present your slide in the talk, it should take 1 minute or less to discuss. This rule is really helpful for planning purposes—a 20-minute presentation should have somewhere around 20 slides. Also, frequently giving your audience new information to feast on helps keep them engaged. During practice, if you find yourself spending more than a minute on a slide, there’s too much for that one slide—it’s time to break up the content into multiple slides or even remove information that is not wholly central to the story you are trying to tell. Reduce, reduce, reduce, until you get to a single message, clearly described, which takes less than 1 minute to present.

Rule 3: Make use of your heading

When each slide conveys only one message, use the heading of that slide to write exactly the message you are trying to deliver. Instead of titling the slide “Results,” try “CTNND1 is central to metastasis” or “False-positive rates are highly sample specific.” Use this landmark signpost to ensure that all the content on that slide is related exactly to the heading and only the heading. Think of the slide heading as the introductory or concluding sentence of a paragraph and the slide content the rest of the paragraph that supports the main point of the paragraph. An audience member should be able to follow along with you in the “paragraph” and come to the same conclusion sentence as your header at the end of the slide.

Rule 4: Include only essential points

While you are speaking, audience members’ eyes and minds will be wandering over your slide. If you have a comment, detail, or figure on a slide, have a plan to explicitly identify and talk about it. If you don’t think it’s important enough to spend time on, then don’t have it on your slide. This is especially important when faculty are present. I often tell students that thesis committee members are like cats: If you put a shiny bauble in front of them, they’ll go after it. Be sure to only put the shiny baubles on slides that you want them to focus on. Putting together a thesis meeting for only faculty is really an exercise in herding cats (if you have cats, you know this is no easy feat). Clear and concise slide design will go a long way in helping you corral those easily distracted faculty members.

Rule 5: Give credit, where credit is due

An exception to Rule 4 is to include proper citations or references to work on your slide. When adding citations, names of other researchers, or other types of credit, use a consistent style and method for adding this information to your slides. Your audience will then be able to easily partition this information from the other content. A common mistake people make is to think “I’ll add that reference later,” but I highly recommend you put the proper reference on the slide at the time you make it, before you forget where it came from. Finally, in certain kinds of presentations, credits can make it clear who did the work. For the faculty members heading labs, it is an effective way to connect your audience with the personnel in the lab who did the work, which is a great career booster for that person. For graduate students, it is an effective way to delineate your contribution to the work, especially in meetings where the goal is to establish your credentials for meeting the rigors of a PhD checkpoint.

Rule 6: Use graphics effectively

As a rule, you should almost never have slides that only contain text. Build your slides around good visualizations. It is a visual presentation after all, and as they say, a picture is worth a thousand words. However, on the flip side, don’t muddy the point of the slide by putting too many complex graphics on a single slide. A multipanel figure that you might include in a manuscript should often be broken into 1 panel per slide (see Rule 1 ). One way to ensure that you use the graphics effectively is to make a point to introduce the figure and its elements to the audience verbally, especially for data figures. For example, you might say the following: “This graph here shows the measured false-positive rate for an experiment and each point is a replicate of the experiment, the graph demonstrates …” If you have put too much on one slide to present in 1 minute (see Rule 2 ), then the complexity or number of the visualizations is too much for just one slide.

Rule 7: Design to avoid cognitive overload

The type of slide elements, the number of them, and how you present them all impact the ability for the audience to intake, organize, and remember the content. For example, a frequent mistake in slide design is to include full sentences, but reading and verbal processing use the same cognitive channels—therefore, an audience member can either read the slide, listen to you, or do some part of both (each poorly), as a result of cognitive overload [ 4 ]. The visual channel is separate, allowing images/videos to be processed with auditory information without cognitive overload [ 6 ] (Rule 6). As presentations are an exercise in listening, and not reading, do what you can to optimize the ability of the audience to listen. Use words sparingly as “guide posts” to you and the audience about major points of the slide. In fact, you can add short text fragments, redundant with the verbal component of the presentation, which has been shown to improve retention [ 7 ] (see Fig 1 for an example of redundant text that avoids cognitive overload). Be careful in the selection of a slide template to minimize accidentally adding elements that the audience must process, but are unimportant. David JP Phillips argues (and effectively demonstrates in his TEDx talk [ 5 ]) that the human brain can easily interpret 6 elements and more than that requires a 500% increase in human cognition load—so keep the total number of elements on the slide to 6 or less. Finally, in addition to the use of short text, white space, and the effective use of graphics/images, you can improve ease of cognitive processing further by considering color choices and font type and size. Here are a few suggestions for improving the experience for your audience, highlighting the importance of these elements for some specific groups:

• Use high contrast colors and simple backgrounds with low to no color—for persons with dyslexia or visual impairment.
• Use sans serif fonts and large font sizes (including figure legends), avoid italics, underlining (use bold font instead for emphasis), and all capital letters—for persons with dyslexia or visual impairment [ 8 ].
• Use color combinations and palettes that can be understood by those with different forms of color blindness [ 9 ]. There are excellent tools available to identify colors to use and ways to simulate your presentation or figures as they might be seen by a person with color blindness (easily found by a web search).
• In this increasing world of virtual presentation tools, consider practicing your talk with a closed captioning system capture your words. Use this to identify how to improve your speaking pace, volume, and annunciation to improve understanding by all members of your audience, but especially those with a hearing impairment.

Rule 8: Design the slide so that a distracted person gets the main takeaway

It is very difficult to stay focused on a presentation, especially if it is long or if it is part of a longer series of talks at a conference. Audience members may get distracted by an important email, or they may start dreaming of lunch. So, it’s important to look at your slide and ask “If they heard nothing I said, will they understand the key concept of this slide?” The other rules are set up to help with this, including clarity of the single point of the slide (Rule 1), titling it with a major conclusion (Rule 3), and the use of figures (Rule 6) and short text redundant to your verbal description (Rule 7). However, with each slide, step back and ask whether its main conclusion is conveyed, even if someone didn’t hear your accompanying dialog. Importantly, ask if the information on the slide is at the right level of abstraction. For example, do you have too many details about the experiment, which hides the conclusion of the experiment (i.e., breaking Rule 1)? If you are worried about not having enough details, keep a slide at the end of your slide deck (after your conclusions and acknowledgments) with the more detailed information that you can refer to during a question and answer period.

Rule 9: Iteratively improve slide design through practice

Well-designed slides that follow the first 8 rules are intended to help you deliver the message you intend and in the amount of time you intend to deliver it in. The best way to ensure that you nailed slide design for your presentation is to practice, typically a lot. The most important aspects of practicing a new presentation, with an eye toward slide design, are the following 2 key points: (1) practice to ensure that you hit, each time through, the most important points (for example, the text guide posts you left yourself and the title of the slide); and (2) practice to ensure that as you conclude the end of one slide, it leads directly to the next slide. Slide transitions, what you say as you end one slide and begin the next, are important to keeping the flow of the “story.” Practice is when I discover that the order of my presentation is poor or that I left myself too few guideposts to remember what was coming next. Additionally, during practice, the most frequent things I have to improve relate to Rule 2 (the slide takes too long to present, usually because I broke Rule 1, and I’m delivering too much information for one slide), Rule 4 (I have a nonessential detail on the slide), and Rule 5 (I forgot to give a key reference). The very best type of practice is in front of an audience (for example, your lab or peers), where, with fresh perspectives, they can help you identify places for improving slide content, design, and connections across the entirety of your talk.

Rule 10: Design to mitigate the impact of technical disasters

The real presentation almost never goes as we planned in our heads or during our practice. Maybe the speaker before you went over time and now you need to adjust. Maybe the computer the organizer is having you use won’t show your video. Maybe your internet is poor on the day you are giving a virtual presentation at a conference. Technical problems are routinely part of the practice of sharing your work through presentations. Hence, you can design your slides to limit the impact certain kinds of technical disasters create and also prepare alternate approaches. Here are just a few examples of the preparation you can do that will take you a long way toward avoiding a complete fiasco:

• Save your presentation as a PDF—if the version of Keynote or PowerPoint on a host computer cause issues, you still have a functional copy that has a higher guarantee of compatibility.
• In using videos, create a backup slide with screen shots of key results. For example, if I have a video of cell migration, I’ll be sure to have a copy of the start and end of the video, in case the video doesn’t play. Even if the video worked, you can pause on this backup slide and take the time to highlight the key results in words if someone could not see or understand the video.
• Avoid animations, such as figures or text that flash/fly-in/etc. Surveys suggest that no one likes movement in presentations [ 3 , 4 ]. There is likely a cognitive underpinning to the almost universal distaste of pointless animations that relates to the idea proposed by Kosslyn and colleagues that animations are salient perceptual units that captures direct attention [ 4 ]. Although perceptual salience can be used to draw attention to and improve retention of specific points, if you use this approach for unnecessary/unimportant things (like animation of your bullet point text, fly-ins of figures, etc.), then you will distract your audience from the important content. Finally, animations cause additional processing burdens for people with visual impairments [ 10 ] and create opportunities for technical disasters if the software on the host system is not compatible with your planned animation.

Conclusions

These rules are just a start in creating more engaging presentations that increase audience retention of your material. However, there are wonderful resources on continuing on the journey of becoming an amazing public speaker, which includes understanding the psychology and neuroscience behind human perception and learning. For example, as highlighted in Rule 7, David JP Phillips has a wonderful TEDx talk on the subject [ 5 ], and “PowerPoint presentation flaws and failures: A psychological analysis,” by Kosslyn and colleagues is deeply detailed about a number of aspects of human cognition and presentation style [ 4 ]. There are many books on the topic, including the popular “Presentation Zen” by Garr Reynolds [ 11 ]. Finally, although briefly touched on here, the visualization of data is an entire topic of its own that is worth perfecting for both written and oral presentations of work, with fantastic resources like Edward Tufte’s “The Visual Display of Quantitative Information” [ 12 ] or the article “Visualization of Biomedical Data” by O’Donoghue and colleagues [ 13 ].

Acknowledgments

I would like to thank the countless presenters, colleagues, students, and mentors from which I have learned a great deal from on effective presentations. Also, a thank you to the wonderful resources published by organizations on how to increase inclusivity. A special thanks to Dr. Jason Papin and Dr. Michael Guertin on early feedback of this editorial.

• View Article
• PubMed/NCBI
• Google Scholar
• 3. Teaching VUC for Making Better PowerPoint Presentations. n.d. Available from: https://cft.vanderbilt.edu/guides-sub-pages/making-better-powerpoint-presentations/#baddeley .
• 8. Creating a dyslexia friendly workplace. Dyslexia friendly style guide. nd. Available from: https://www.bdadyslexia.org.uk/advice/employers/creating-a-dyslexia-friendly-workplace/dyslexia-friendly-style-guide .
• 9. Cravit R. How to Use Color Blind Friendly Palettes to Make Your Charts Accessible. 2019. Available from: https://venngage.com/blog/color-blind-friendly-palette/ .
• 10. Making your conference presentation more accessible to blind and partially sighted people. n.d. Available from: https://vocaleyes.co.uk/services/resources/guidelines-for-making-your-conference-presentation-more-accessible-to-blind-and-partially-sighted-people/ .
• 11. Reynolds G. Presentation Zen: Simple Ideas on Presentation Design and Delivery. 2nd ed. New Riders Pub; 2011.
• 12. Tufte ER. The Visual Display of Quantitative Information. 2nd ed. Graphics Press; 2001.

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Presenting your research effectively

Here's how to home in on your key message and present it in a clear, engaging way.

By Richard Chambers

Print version: page 28

For many graduate students, presenting their research is a daunting task. How do you cram your months' worth of data collection and analysis into a 10- to 20-minute presentation? Deciding what information to include and how to organize it can be more stressful than actually giving the presentation.

But anyone filled with presentation anxiety should remember that the difficult part is already over once it comes time to present. No one knows your research better than you, and those who come to listen to your presentation are probably there because they are interested in your research, not because they are required to be there. Taking this perspective can make presenting your research much less stressful because the focus of the task is no longer to engage an uninterested audience: It is to keep an already interested audience engaged.

Here are some suggestions for constructing a presentation using various multimedia tools, such as PowerPoint, Keynote and Prezi.

Planning: What should be included?

First, it is always important to refer to the APA Publication Manual as well as to your specific conference's guidelines. Second, before you start building any presentation, consider your audience. Will it be scientists who are familiar with your research area or will it be people who may never have had a class in psychology? Based on the answer, you will want to make sure you structure your presentation with the appropriate depth and terminology.

Determining the main messages you want to communicate in your presentation is often the next step in organizing your thoughts. As you create your presentation, sometimes it is difficult to determine whether a particular piece of information is important or necessary. Consider the value added by each piece of content as you determine whether to include it or not. Often, the background and theory for your research must be presented concisely so that you have time to present your study and findings. Ten minutes is not much time, so emphasize the main points so that your audience has a clear understanding of your take-home messages. When you start planning, writing out content on individual Post-it Notes can be a great way to visually organize your thoughts and, ultimately, your presentation.

Building slides: The do's and don'ts

After you've decided on your content, the real fun begins: designing slides. There are no rules for how to build a slide, but here are a few suggestions to keep in mind:

Tell your story simply

Remember that you want to tell a story, not lecture people. The oral presentation as a whole should be the work of art, and the slides should be supplementary to the story you are trying to convey. When laying out content and designing slides, remember that less is more. Having more slides with less content on each will help keep your audience focused more on what you are saying and prevent them from staring blankly at your slides.

Consider the billboard

Marketers try to use only three seconds' worth of content, the same amount of time a driver has to view a billboard. Your audience may not be driving cars, but you want them to stay engaged with your story, and this makes the three-seconds rule a good one to apply when building a slide. If it takes more than three seconds to read the slide, consider revising it.

Keep it clean

White space will help the slide appear cleaner and more aesthetically appealing. It is important to note that white space may not always be white. Each presentation should have its own color palette that consists of approximately three complementary colors. Try not to use more than three colors, and be aware of the emotion certain colors may evoke. For example, blue is the color of the sky and the ocean and is typically a soothing and relaxing color; red, on the other hand, is a bold, passionate color that may evoke more aggressive feelings.

Don't get too lively

Animation is another customizable option of presentations, but it may not be worth the effort. Animation can be distracting, making it difficult for the audience to stay with the story being told. When in doubt about animation, remember to ask what value is being added. There may be times when you really want to add emphasis to a specific word or phrase. If this is the case, and you deem it necessary, animation may be an acceptable choice. For example, the "grow" feature may be useful for adding emphasis to a word or phrase.

It is important to have highly readable slides with good contrast between the words and background. Choose a font that is easy to read, and be aware that each font has a different personality and sends a different message. The personality of some fonts may even be considered inappropriate for certain settings. For example, the font Comic Sans is a "lighter" font and would most likely not be a wise choice for a presentation at a conference.

Other important considerations include typesetting and the spacing of letters, words and lines. These all affect readability but can also be used as a way to add emphasis. Sometimes you may feel a need to use bullet points. Do not. Typesetting can replace bullet points and add extra distinction to each line of content without cluttering the slide with bullets. For example, consider bolding and increasing the font size of parent lines and indenting child lines.

If you find that your slides contain mainly words, remember that a picture, chart or diagram can augment the text. People often depend on vision as their primary sense; this means your audience has a potential preference for visual information other than just words on the screen.

Presenting data: Think about what kind of graph is best

When you share information, specifically about data, bar graphs should usually be your first choice, with scatter plots a close second because they are simple. The same suggestion about having more slides with less content on each applies to charts and graphs. If the graph or chart will look cleaner as two graphs instead of one, use two graphs.

Accuracy of a graph is, of course, important. For example, it is easy to convey the wrong message simply by altering the range of the y-axis. A restricted y-axis can make the differences between groups look much larger than they actually are to those audience members who do not look closely. It is always important to be ethical and to ensure that information, especially about data, is not being misrepresented. Strive to make charts and graphs easily interpretable, and try not to clutter them with additional numbers.

Building presentations does not need to be a challenge. Presenting should be an opportunity to share with others something very important to you — your research. These suggestions can be used as a starting point to guide the development of future research presentations and to help relieve some of the stress surrounding them.

Richard Chambers is the industrial/organizational psychology representative on the APA Student Science Council. He is a doctoral student at Louisiana Tech University. 

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Advanced Research Methods

• Presenting the Research Paper
• What Is Research?
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• Writing the Research Paper

Writing an Abstract

Oral presentation, compiling a powerpoint.

Abstract : a short statement that describes a longer work.

• Indicate the subject.
• Describe the purpose of the investigation.
• Briefly discuss the method used.
• Make a statement about the result.

Oral presentations usually introduce a discussion of a topic or research paper. A good oral presentation is focused, concise, and interesting in order to trigger a discussion.

• Be well prepared; write a detailed outline.
• Introduce the subject.
• Talk about the sources and the method.
• Indicate if there are conflicting views about the subject (conflicting views trigger discussion).
• Make a statement about your new results (if this is your research paper).
• Use visual aids or handouts if appropriate.

An effective PowerPoint presentation is just an aid to the presentation, not the presentation itself .

• Be brief and concise.
• Focus on the subject.
• Attract attention; indicate interesting details.
• If possible, use relevant visual illustrations (pictures, maps, charts graphs, etc.).
• Use bullet points or numbers to structure the text.
• Make clear statements about the essence/results of the topic/research.
• Don't write down the whole outline of your paper and nothing else.
• Don't write long full sentences on the slides.
• Don't use distracting colors, patterns, pictures, decorations on the slides.
• Don't use too complicated charts, graphs; only those that are relatively easy to understand.
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• Chapter Seven: Presenting Your Results

This chapter serves as the culmination of the previous chapters, in that it focuses on how to present the results of one's study, regardless of the choice made among the three methods. Writing in academics has a form and style that you will want to apply not only to report your own research, but also to enhance your skills at reading original research published in academic journals. Beyond the basic academic style of report writing, there are specific, often unwritten assumptions about how quantitative, qualitative, and critical/rhetorical studies should be organized and the information they should contain. This chapter discusses how to present your results in writing, how to write accessibly, how to visualize data, and how to present your results in person.  

• Chapter One: Introduction
• Chapter Two: Understanding the distinctions among research methods
• Chapter Three: Ethical research, writing, and creative work
• Chapter Four: Quantitative Methods (Part 1)
• Chapter Four: Quantitative Methods (Part 2 - Doing Your Study)
• Chapter Four: Quantitative Methods (Part 3 - Making Sense of Your Study)
• Chapter Five: Qualitative Methods (Part 1)
• Chapter Five: Qualitative Data (Part 2)
• Chapter Six: Critical / Rhetorical Methods (Part 1)
• Chapter Six: Critical / Rhetorical Methods (Part 2)

Written Presentation of Results

Once you've gone through the process of doing communication research – using a quantitative, qualitative, or critical/rhetorical methodological approach – the final step is to  communicate  it.

The major style manuals (the APA Manual, the MLA Handbook, and Turabian) are very helpful in documenting the structure of writing a study, and are highly recommended for consultation. But, no matter what style manual you may use, there are some common elements to the structure of an academic communication research paper.

Title Page :

This is simple: Your Paper's Title, Your Name, Your Institutional Affiliation (e.g., University), and the Date, each on separate lines, centered on the page. Try to make your title both descriptive (i.e., it gives the reader an idea what the study is about) and interesting (i.e., it is catchy enough to get one's attention).

For example, the title, "The uncritical idealization of a compensated psychopath character in a popular book series," would not be an inaccurate title for a published study, but it is rather vague and exceedingly boring. That study's author fortunately chose the title, "A boyfriend to die for: Edward Cullen as compensated psychopath in Stephanie Meyer's  Twilight ," which is more precisely descriptive, and much more interesting (Merskin, 2011). The use of the colon in academic titles can help authors accomplish both objectives: a catchy but relevant phrase, followed by a more clear explanation of the article's topic.

In some instances, you might be asked to write an abstract, which is a summary of your paper that can range in length from 75 to 250 words. If it is a published paper, it is useful to include key search terms in this brief description of the paper (the title may already have a few of these terms as well). Although this may be the last thing your write, make it one of the best things you write, because this may be the first thing your audience reads about the paper (and may be the only thing read if it is written badly). Summarize the problem/research question, your methodological approach, your results and conclusions, and the significance of the paper in the abstract.

Quantitative and qualitative studies will most typically use the rest of the section titles noted below. Critical/rhetorical studies will include many of the same steps, but will often have different headings. For example, a critical/rhetorical paper will have an introduction, definition of terms, and literature review, followed by an analysis (often divided into sections by areas of investigation) and ending with a conclusion/implications section. Because critical/rhetorical research is much more descriptive, the subheadings in such a paper are often times not generic subheads like "literature review," but instead descriptive subheadings that apply to the topic at hand, as seen in the schematic below. Because many journals expect the article to follow typical research paper headings of introduction, literature review, methods, results, and discussion, we discuss these sections briefly next.

Introduction:

As you read social scientific journals (see chapter 1 for examples), you will find that they tend to get into the research question quickly and succinctly. Journal articles from the humanities tradition tend to be more descriptive in the introduction. But, in either case, it is good to begin with some kind of brief anecdote that gets the reader engaged in your work and lets the reader understand why this is an interesting topic. From that point, state your research question, define the problem (see Chapter One) with an overview of what we do and don't know, and finally state what you will do, or what you want to find out. The introduction thus builds the case for your topic, and is the beginning of building your argument, as we noted in chapter 1.

By the end of the Introduction, the reader should know what your topic is, why it is a significant communication topic, and why it is necessary that you investigate it (e.g., it could be there is gap in literature, you will conduct valuable exploratory research, or you will provide a new model for solving some professional or social problem).

Literature Review:

The literature review summarizes and organizes the relevant books, articles, and other research in this area. It sets up both quantitative and qualitative studies, showing the need for the study. For critical/rhetorical research, the literature review often incorporates the description of the historical context and heuristic vocabulary, with key terms defined in this section of the paper. For more detail on writing a literature review, see Appendix 1.

The methods of your paper are the processes that govern your research, where the researcher explains what s/he did to solve the problem. As you have seen throughout this book, in communication studies, there are a number of different types of research methods. For example, in quantitative research, one might conduct surveys, experiments, or content analysis. In qualitative research, one might instead use interviews and observations. Critical/rhetorical studies methods are more about the interpretation of texts or the study of popular culture as communication. In creative communication research, the method may be an interpretive performance studies or filmmaking. Other methods used sometimes alone, or in combination with other methods, include legal research, historical research, and political economy research.

In quantitative and qualitative research papers, the methods will be most likely described according to the APA manual standards. At the very least, the methods will include a description of participants, data collection, and data analysis, with specific details on each of these elements. For example, in an experiment, the researcher will describe the number of participants, the materials used, the design of the experiment, the procedure of the experiment, and what statistics will be used to address the hypotheses/research questions.

Critical/rhetorical researchers rarely have a specific section called "methods," as opposed to quantitative and qualitative researchers, but rather demonstrate the method they use for analysis throughout the writing of their piece.

Helping your reader understand the methods you used for your study is important not only for your own study's credibility, but also for possible replication of your study by other researchers. A good guideline to keep in mind is  transparency . You want to be as clear as possible in describing the decisions you made in designing your study, gathering and analyzing your data so that the reader can retrace your steps and understand how you came to the conclusions you formed. A research study can be very good, but if it is not clearly described so that others can see how the results were determined or obtained, then the quality of the study and its potential contributions are lost.

After you completed your study, your findings will be listed in the results section. Particularly in a quantitative study, the results section is for revisiting your hypotheses and reporting whether or not your results supported them, and the statistical significance of the results. Whether your study supported or contradicted your hypotheses, it's always helpful to fully report what your results were. The researcher usually organizes the results of his/her results section by research question or hypothesis, stating the results for each one, using statistics to show how the research question or hypothesis was answered in the study.

The qualitative results section also may be organized by research question, but usually is organized by themes which emerged from the data collected. The researcher provides rich details from her/his observations and interviews, with detailed quotations provided to illustrate the themes identified. Sometimes the results section is combined with the discussion section.

Critical/rhetorical researchers would include their analysis often with different subheadings in what would be considered a "results" section, yet not labeled specifically this way.

Discussion:

In the discussion section, the researcher gives an appraisal of the results. Here is where the researcher considers the results, particularly in light of the literature review, and explains what the findings mean. If the results confirmed or corresponded with the findings of other literature, then that should be stated. If the results didn't support the findings of previous studies, then the researcher should develop an explanation of why the study turned out this way. Sometimes, this section is called a "conclusion" by researchers.

References:

In this section, all of the literature cited in the text should have full references in alphabetical order. Appendices: Appendix material includes items like questionnaires used in the study, photographs, documents, etc. An alphabetical letter is assigned for each piece (e.g. Appendix A, Appendix B), with a second line of title describing what the appendix contains (e.g. Participant Informed Consent, or  New York Times  Speech Coverage). They should be organized consistently with the order in which they are referenced in the text of the paper. The page numbers for appendices are consecutive with the paper and reference list.

Tables/Figures:

Tables and figures are referenced in the text, but included at the end of the study and numbered consecutively. (Check with your professor; some like to have tables and figures inserted within the paper's main text.) Tables generally are data in a table format, whereas figures are diagrams (such as a pie chart) and drawings (such as a flow chart).

Accessible Writing

As you may have noticed, academic writing does have a language (e.g., words like heuristic vocabulary and hypotheses) and style (e.g., literature reviews) all its own. It is important to engage in that language and style, and understand how to use it to  communicate effectively in an academic context . Yet, it is also important to remember that your analyses and findings should also be written to be accessible. Writers should avoid excessive jargon, or—even worse—deploying jargon to mask an incomplete understanding of a topic.

The scourge of excessive jargon in academic writing was the target of a famous hoax in 1996. A New York University physics professor submitted an article, " Transgressing the Boundaries: Toward a Transformative Hermeneutics of Quantum Gravity ," to a special issue of the academic journal  Social Text  devoted to science and postmodernism. The article was designed to point out how dense academic jargon can sometimes mask sloppy thinking. As the professor, Alan Sokal, had expected, the article was published. One sample sentence from the article reads:

It has thus become increasingly apparent that physical "reality", no less than social "reality", is at bottom a social and linguistic construct; that scientific "knowledge", far from being objective, reflects and encodes the dominant ideologies and power relations of the culture that produced it; that the truth claims of science are inherently theory-laden and self-referential; and consequently, that the discourse of the scientific community, for all its undeniable value, cannot assert a privileged epistemological status with respect to counter-hegemonic narratives emanating from dissident or marginalized communities. (Sokal, 1996. pp. 217-218)

According to the journal's editor, about six reviewers had read the article but didn't suspect that it was phony. A public debate ensued after Sokal revealed his hoax. Sokal said he worried that jargon and intellectual fads cause academics to lose contact with the real world and "undermine the prospect for progressive social critique" ( Scott, 1996 ). The APA Manual recommends to avoid using technical vocabulary where it is not needed or relevant or if the technical language is overused, thus becoming jargon. In short, the APA argues that "scientific jargon...grates on the reader, encumbers the communication of information, and wastes space" (American Psychological Association, 2010, p. 68).

Data Visualization

Images and words have long existed on the printed page of manuscripts, yet, until recently, relatively few researchers possessed the resources to effectively combine images combined with words (Tufte, 1990, 1983). Communication scholars are only now becoming aware of this dimension in research as computer technologies have made it possible for many people to produce and publish multimedia presentations.

Although visuals may seem to be anathema to the primacy of the written word in research, they are a legitimate way, and at times the best way, to present ideas. Visual scholar Lester Faigley et al. (2004) explains how data visualizations have become part of our daily lives:

Visualizations can shed light on research as well. London-based David McCandless specializes in visualizing interesting research questions, or in his words "the questions I wanted answering" (2009, p. 7). His images include a graph of the  peak times of the year for breakups  (based on Facebook status updates), a  radiation dosage chart , and some  experiments with the Google Ngram Viewer , which charts the appearance of keywords in millions of books over hundreds of years.

The  public domain image  below creatively maps U.S. Census data of the outflow of people from California to other states between 1995 and 2000.

Visualizing one's research is possible in multiple ways. A simple technology, for example, is to enter data into a spreadsheet such as Excel, and select  Charts  or  SmartArt  to generate graphics. A number of free web tools can also transform raw data into useful charts and graphs.  Many Eyes , an open source data visualization tool (sponsored by IBM Research), says its goal "is to 'democratize' visualization and to enable a new social kind of data analysis" (IBM, 2011). Another tool,  Soundslides , enables users to import images and audio to create a photographic slideshow, while the program handles all of the background code. Other tools, often open source and free, can help visual academic research into interactive maps; interactive, image-based timelines; interactive charts; and simple 2-D and 3-D animations. Adobe Creative Suite (which includes popular software like Photoshop) is available on most computers at universities, but open source alternatives exist as well.  Gimp  is comparable to Photoshop, and it is free and relatively easy to use.

One online performance studies journal,  Liminalities , is an excellent example of how "research" can be more than just printed words. In each issue, traditional academic essays and book reviews are often supported photographs, while other parts of an issue can include video, audio, and multimedia contributions. The journal, founded in 2005, treats performance itself as a methodology, and accepts contribution in html, mp3, Quicktime, and Flash formats.

For communication researchers, there is also a vast array of visual digital archives available online. Many of these archives are located at colleges and universities around the world, where digital librarians are spearheading a massive effort to make information—print, audio, visual, and graphic—available to the public as part of a global information commons. For example, the University of Iowa has a considerable digital archive including historical photos documenting American railroads and a database of images related to geoscience. The University of Northern Iowa has a growing Special Collections Unit that includes digital images of every UNI Yearbook between 1905 and 1923 and audio files of UNI jazz band performances. Researchers at he University of Michigan developed  OAIster , a rich database that has joined thousands of digital archives in one searchable interface. Indeed, virtually every academic library is now digitizing all types of media, not just texts, and making them available for public viewing and, when possible, for use in presenting research. In addition to academic collections, the  Library of Congress  and the  National Archives  offer an ever-expanding range of downloadable media; commercial, user-generated databases such as Flickr, Buzznet, YouTube and Google Video offer a rich resource of images that are often free of copyright constraints (see Chapter 3 about Creative Commons licenses) and nonprofit endeavors, such as the  Internet Archive , contain a formidable collection of moving images, still photographs, audio files (including concert recordings), and open source software.

Presenting your Work in Person

As Communication students, it's expected that you are not only able to communicate your research project in written form but also in person.

Before you do any oral presentation, it's good to have a brief "pitch" ready for anyone who asks you about your research. The pitch is routine in Hollywood: a screenwriter has just a few minutes to present an idea to a producer. Although your pitch will be more sophisticated than, say, " Snakes on a Plane " (which unfortunately was made into a movie), you should in just a few lines be able to explain the gist of your research to anyone who asks. Developing this concise description, you will have some practice in distilling what might be a complicated topic into one others can quickly grasp.

Oral presentation

In most oral presentations of research, whether at the end of a semester, or at a research symposium or conference, you will likely have just 10 to 20 minutes. This is probably not enough time to read the entire paper aloud, which is not what you should do anyway if you want people to really listen (although, unfortunately some make this mistake). Instead, the point of the presentation should be to present your research in an interesting manner so the listeners will want to read the whole thing. In the presentation, spend the least amount of time on the literature review (a very brief summary will suffice) and the most on your own original contribution. In fact, you may tell your audience that you are only presenting on one portion of the paper, and that you would be happy to talk more about your research and findings in the question and answer session that typically follows. Consider your presentation the beginning of a dialogue between you and the audience. Your tone shouldn't be "I have found everything important there is to find, and I will cram as much as I can into this presentation," but instead "I found some things you will find interesting, but I realize there is more to find."

Turabian (2007) has a helpful chapter on presenting research. Most important, she emphasizes, is to remember that your audience members are listeners, not readers. Thus, recall the lessons on speech making in your college oral communication class. Give an introduction, tell them what the problem is, and map out what you will present to them. Organize your findings into a few points, and don't get bogged down in minutiae. (The minutiae are for readers to find if they wish, not for listeners to struggle through.) PowerPoint slides are acceptable, but don't read them. Instead, create an outline of a few main points, and practice your presentation.

Turabian  suggests an introduction of not more than three minutes, which should include these elements:

• The research topic you will address (not more than a minute).
• Your research question (30 seconds or less)
• An answer to "so what?" – explaining the relevance of your research (30 seconds)
• Your claim, or argument (30 seconds or less)
• The map of your presentation structure (30 seconds or less)

As Turabian (2007) suggests, "Rehearse your introduction, not only to get it right, but to be able to look your audience in the eye as you give it. You can look down at notes later" (p. 125).

Poster presentation

In some symposiums and conferences, you may be asked to present at a "poster" session. Instead of presenting on a panel of 4-5 people to an audience, a poster presenter is with others in a large hall or room, and talks one-on-one with visitors who look at the visual poster display of the research. As in an oral presentation, a poster highlights just the main point of the paper. Then, if visitors have questions, the author can informally discuss her/his findings.

To attract attention, poster presentations need to be nicely designed, or in the words of an advertising professor who schedules poster sessions at conferences, "be big, bold, and brief" ( Broyles , 2011). Large type (at least 18 pt.), graphics, tables, and photos are recommended.

A poster presentation session at a conference, by David Eppstein (Own work) [CC-BY-SA-3.0 ( www.creativecommons.org/licenses/by-sa/3.0 )], via Wikimedia Commons]

The Association for Education in Journalism and Mass Communication (AEJMC) has a  template for making an effective poster presentation . Many universities, copy shops, and Internet services also have large-scale printers, to print full-color research poster designs that can be rolled up and transported in a tube.

Judging Others' Research

After taking this course, you should have a basic knowledge of research methods. There will still be some things that may mystify you as a reader of other's research. For example, you may not be able to interpret the coefficients for statistical significance, or make sense of a complex structural equation. Some specialized vocabulary may still be difficult.

But, you should understand how to critically review research. For example, imagine you have been asked to do a blind (i.e., the author's identity is concealed) "peer review" of communication research for acceptance to a conference, or publication in an academic journal. For most  conferences  and  journals , submissions are made online, where editors can manage the flow and assign reviews to papers. The evaluations reviewers make are based on the same things that we have covered in this book. For example, the conference for the AEJMC ask reviewers to consider (on a five-point scale, from Excellent to Poor) a number of familiar research dimensions, including the paper's clarity of purpose, literature review, clarity of research method, appropriateness of research method, evidence presented clearly, evidence supportive of conclusions, general writing and organization, and the significance of the contribution to the field.

Beyond academia, it is likely you will more frequently apply the lessons of research methods as a critical consumer of news, politics, and everyday life. Just because some expert cites a number or presents a conclusion doesn't mean it's automatically true. John Allen Paulos, in his book  A Mathematician reads the newspaper , suggests some basic questions we can ask. "If statistics were presented, how were they obtained? How confident can we be of them? Were they derived from a random sample or from a collection of anecdotes? Does the correlation suggest a causal relationship, or is it merely a coincidence?" (1997, p. 201).

Through the study of research methods, we have begun to build a critical vocabulary and understanding to ask good questions when others present "knowledge." For example, if Candidate X won a straw poll in Iowa, does that mean she'll get her party's nomination? If Candidate Y wins an open primary in New Hampshire, does that mean he'll be the next president? If Candidate Z sheds a tear, does it matter what the context is, or whether that candidate is a man or a woman? What we learn in research methods about validity, reliability, sampling, variables, research participants, epistemology, grounded theory, and rhetoric, we can consider whether the "knowledge" that is presented in the news is a verifiable fact, a sound argument, or just conjecture.

American Psychological Association (2010). Publication manual of the American Psychological Association (6th ed.). Washington, DC: Author.

Broyles, S. (2011). "About poster sessions." AEJMC.  http://www.aejmc.org/home/2013/01/about-poster-sessions/ .

Faigley, L., George, D., Palchik, A., Selfe, C. (2004).  Picturing texts . New York: W.W. Norton & Company.

IBM (2011). Overview of Many Eyes.  http://www.research.ibm.com/social/projects_manyeyes.shtml .

McCandless, D. (2009).  The visual miscellaneum . New York: Collins Design.

Merskin, D. (2011). A boyfriend to die for: Edward Cullen as compensated psychopath in Stephanie Meyer's  Twilight. Journal of Communication Inquiry  35: 157-178. doi:10.1177/0196859911402992

Paulos, J. A. (1997).  A mathematician reads the newspaper . New York: Anchor.

Scott, J. (1996, May 18). Postmodern gravity deconstructed, slyly.  New York Times , http://www.nytimes.com/books/98/11/15/specials/sokal-text.html .

Sokal, A. (1996). Transgressing the boundaries: towards a transformative hermeneutics of quantum gravity.  Social Text  46/47, 217-252.

Tufte, E. R. (1990).  Envisioning information . Cheshire, CT: Graphics Press.

Tufte, E. R. (1983).  The visual display of quantitative information . Cheshire, CT: Graphics Press.

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How to Present Your Research (Guidelines and Tips)

Published on 01 Feb 2023

Presenting at a conference can be stressful, but can lead to many opportunities, which is why coming prepared is super beneficial.

The internet is full to the brim with tips for making a good presentation. From what you wear to how you stand to good slide design, there’s no shortage of advice to make any old presentation come to life. 

But, not all presentations are created equal. Research presentations, in particular, are unique. 

Communicating complex concepts to an audience with a varied range of awareness about your research topic can be tricky. A lack of guidance and preparation can ruin your chance to share important information with a conference community. This could mean lost opportunities in collaboration or funding or lost confidence in yourself and your work.

So, we’ve put together a list of tips with research presentations in mind. Here’s our top to-do’s when preparing to present your research.

Take every research presentation opportunity

The worst thing you could do for your research is to not present it at all. As intimidating as it can be to get up in front of an audience, you shouldn’t let that stop you from seizing a good opportunity to share your work with a wider community.

These contestants from the Vitae Three Minute Thesis Competition have some great advice to share on taking every possible chance to talk about your research. 

Double-check your research presentation guidelines

Before you get started on your presentation, double-check if you’ve been given guidelines for it. 

If you don’t have specific guidelines for the context of your presentation, we’ve put together a general outline to help you get started. It’s made with the assumption of a 10-15 minute presentation time. So, if you have longer to present, you can always extend important sections or talk longer on certain slides:

• Title Slide (1 slide) - This is a placeholder to give some visual interest and display the topic until your presentation begins.
• Short Introduction (2-3 slides) - This is where you pique the interest of your audience and establish the key questions your presentation covers. Give context to your study with a brief review of the literature (focus on key points, not a full review). If your study relates to any particularly relevant issues, mention it here to increase the audience's interest in the topic.
• Hypothesis (1 slide) - Clearly state your hypothesis.
• Description of Methods (2-3 slides) - Clearly, but briefly, summarize your study design including a clear description of the study population, the sample size and any instruments or manipulations to gather the data.
• Results and Data Interpretation (2-4 slides) - Illustrate your results through simple tables, graphs, and images. Remind the audience of your hypothesis and discuss your interpretation of the data/results.
• Conclusion (2-3 slides) - Further interpret your results. If you had any sources of error or difficulties with your methods, discuss them here and address how they could be (or were) improved. Discuss your findings as part of the bigger picture and connect them to potential further outcomes or areas of study.
• Closing (1 slide) - If anyone supported your research with guidance, awards, or funding, be sure to recognize their contribution. If your presentation includes a Q&A session, open the floor to questions.

Plan for about one minute for each slide of information that you have. Be sure that you don’t cram your slides with text (stick to bullet points and images to emphasize key points).

And, if you’re looking for more inspiration to help you in scripting an oral research presentation. University of Virginia has a helpful oral presentation outline script .

A PhD Student working on an upcoming oral presentation.

Put yourself in your listeners shoes

As mentioned in the intro, research presentations are unique because they deal with specialized topics and complicated concepts. There’s a good chance that a large section of your audience won’t have the same understanding of your topic area as you do. So, do your best to understand where your listeners are at and adapt your language/definitions to that.

There’s an increasing awareness around the importance of scientific communication. Comms experts have even started giving TED Talks on how to bridge the gap between science and the public (check out Talk Nerdy to Me ). A general communication tip is to find out what sort of audience will listen to your talk. Then, beware of using jargon and acronyms unless you're 100% certain that your audience knows what they mean. 

On the other end of the spectrum, you don’t want to underestimate your audience. Giving too much background or spending ages summarizing old work to a group of experts in the field would be a waste of valuable presentation time (and would put you at risk of losing your audience's interest). 

Finally, if you can, practice your presentation on someone with a similar level of topic knowledge to the audience you’ll be presenting to.

Use scientific storytelling in your presentation

In scenarios where it’s appropriate, crafting a story allows you to break free from the often rigid tone of scientific communications. It helps your brain hit the refresh button and observe your findings from a new perspective. Plus, it can be a lot of fun to do!

If you have a chance to use scientific storytelling in your presentation, take full advantage of it. The best way to weave a story for your audience into a presentation is by setting the scene during your introduction. As you set the context of your research, set the context of your story/example at the same time. Continue drawing those parallels as you present. Then, deliver the main message of the story (or the “Aha!”) moment during your presentation’s conclusion.

If delivered well, a good story will keep your audience on the edge of their seats and glued to your entire presentation.

Emphasize the “Why” (not the “How”) of your research

Along the same lines as using storytelling, it’s important to think of WHY your audience should care about your work. Find ways to connect your research to valuable outcomes in society. Take your individual points on each slide and bring things back to the bigger picture. Constantly remind your listeners how it’s all connected and why that’s important.

One helpful way to get in this mindset is to look back to the moment before you became an expert on your topic. What got you interested? What was the reason for asking your research question? And, what motivated you to power through all the hard work to come? Then, looking forward, think about what key takeaways were most interesting or surprised you the most. How can these be applied to impact positive change in your research field or the wider community?

Be picky about what you include

It’s tempting to discuss all the small details of your methods or findings. Instead, focus on the most important information and takeaways that you think your audience will connect with. Decide on these takeaways before you script your presentation so that you can set the scene properly and provide only the information that has an added value.

When it comes to choosing data to display in your presentation slides, keep it simple. Wherever possible, use visuals to communicate your findings as opposed to large tables filled with numbers. This article by Richard Chambers has some great tips on using visuals in your slides and graphs.

Hide your complex tables and data in additional slides

With the above tip in mind: Just because you don’t include data and tables in your main presentation slides, doesn’t mean you can’t keep them handy for reference. If there’s a Q&A session after your presentation (or if you’ll be sharing your slides to view on-demand after) one great trick is to include additional slides/materials after your closing slide. You can keep these in your metaphorical “back pocket” to refer to if a specific question is asked about a data set or method. They’re also handy for people viewing your presentation slides later that might want to do a deeper dive into your methods/results.

However, just because you have these extra slides doesn’t mean you shouldn’t make the effort to make that information more accessible. A research conference platform like Fourwaves allows presenters to attach supplementary materials (figures, posters, slides, videos and more) that conference participants can access anytime.

Leave your audience with (a few) questions

Curiosity is a good thing. Whether you have a Q&A session or not, you should want to leave your audience with a few key questions. The most important one:

“Where can I find out more?”

Obviously, it’s important to answer basic questions about your research context, hypothesis, methods, results, and interpretation. If you answer these while focusing on the “Why?” and weaving a good story, you’ll be setting the stage for an engaging Q&A session and/or some great discussions in the halls after your presentation. Just be sure that you have further links or materials ready to provide to those who are curious. 

Conclusion: The true expert in your research presentation

Throughout the entire process of scripting, creating your slides, and presenting, it’s important to remember that no one knows your research better than you do. If you’re nervous, remind yourself that the people who come to listen to your presentation are most likely there due to a genuine interest in your work. The pressure isn’t to connect with an uninterested audience - it’s to make your research more accessible and relevant for an already curious audience.

Finally, to practice what we preached in our last tip: If you’re looking to learn more about preparing for a research presentation, check out our articles on how to dress for a scientific conference and general conference presentation tips .

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How to Structure a Scientific Article, Conference Poster and Presentation

• First Online: 22 April 2022

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The main aim of any research endeavor is to disseminate research findings among the scientific community and enrich scientific knowledge. The two most common scientific dissemination modes are journal articles and conference proceedings. Whichever route you decide to follow, the scientific writing that needs to be partaken typically follows the IMRaD format, i.e., Introduction, Methods, Results, and Discussion-Conclusion. This format is applicable for abstracts (unless an unstructured abstract is specifically required), scientific research articles, and conference poster or oral presentations. Understanding the importance of each IMRaD section along with the role of a clear and informative title is your road to success. This also applies to proper citation of other people’s work, ideas, and thoughts. This chapter will guide you, through a stepwise approach, to understand the various elements that make up a successful scientific article, an abstract, a conference poster, and an oral presentation.

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Ways to give an effective seminar about your research project

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In my first year of graduate school, I was terrified of giving presentations. I would put too much information on my slides, talk too fast and constantly forget or trip over certain words. Unsuprisingly, the reception was lukewarm at best.

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How to Make a PowerPoint Presentation of Your Research Paper

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Table of Contents

A research paper presentation is often used at conferences and in other settings where you have an opportunity to share your research, and get feedback from your colleagues. Although it may seem as simple as summarizing your research and sharing your knowledge, successful research paper PowerPoint presentation examples show us that there’s a little bit more than that involved.

In this article, we’ll highlight how to make a PowerPoint presentation from a research paper, and what to include (as well as what NOT to include). We’ll also touch on how to present a research paper at a conference.

Purpose of a Research Paper Presentation

The purpose of presenting your paper at a conference or forum is different from the purpose of conducting your research and writing up your paper. In this setting, you want to highlight your work instead of including every detail of your research. Likewise, a presentation is an excellent opportunity to get direct feedback from your colleagues in the field. But, perhaps the main reason for presenting your research is to spark interest in your work, and entice the audience to read your research paper.

So, yes, your presentation should summarize your work, but it needs to do so in a way that encourages your audience to seek out your work, and share their interest in your work with others. It’s not enough just to present your research dryly, to get information out there. More important is to encourage engagement with you, your research, and your work.

Tips for Creating Your Research Paper Presentation

In addition to basic PowerPoint presentation recommendations, which we’ll cover later in this article, think about the following when you’re putting together your research paper presentation:

• Know your audience : First and foremost, who are you presenting to? Students? Experts in your field? Potential funders? Non-experts? The truth is that your audience will probably have a bit of a mix of all of the above. So, make sure you keep that in mind as you prepare your presentation.

Know more about: Discover the Target Audience .

• Your audience is human : In other words, they may be tired, they might be wondering why they’re there, and they will, at some point, be tuning out. So, take steps to help them stay interested in your presentation. You can do that by utilizing effective visuals, summarize your conclusions early, and keep your research easy to understand.
• Running outline : It’s not IF your audience will drift off, or get lost…it’s WHEN. Keep a running outline, either within the presentation or via a handout. Use visual and verbal clues to highlight where you are in the presentation.
• Where does your research fit in? You should know of work related to your research, but you don’t have to cite every example. In addition, keep references in your presentation to the end, or in the handout. Your audience is there to hear about your work.
• Plan B : Anticipate possible questions for your presentation, and prepare slides that answer those specific questions in more detail, but have them at the END of your presentation. You can then jump to them, IF needed.

What Makes a PowerPoint Presentation Effective?

You’ve probably attended a presentation where the presenter reads off of their PowerPoint outline, word for word. Or where the presentation is busy, disorganized, or includes too much information. Here are some simple tips for creating an effective PowerPoint Presentation.

• Less is more: You want to give enough information to make your audience want to read your paper. So include details, but not too many, and avoid too many formulas and technical jargon.
• Clean and professional : Avoid excessive colors, distracting backgrounds, font changes, animations, and too many words. Instead of whole paragraphs, bullet points with just a few words to summarize and highlight are best.
• Know your real-estate : Each slide has a limited amount of space. Use it wisely. Typically one, no more than two points per slide. Balance each slide visually. Utilize illustrations when needed; not extraneously.
• Keep things visual : Remember, a PowerPoint presentation is a powerful tool to present things visually. Use visual graphs over tables and scientific illustrations over long text. Keep your visuals clean and professional, just like any text you include in your presentation.

Know more about our Scientific Illustrations Services .

Another key to an effective presentation is to practice, practice, and then practice some more. When you’re done with your PowerPoint, go through it with friends and colleagues to see if you need to add (or delete excessive) information. Double and triple check for typos and errors. Know the presentation inside and out, so when you’re in front of your audience, you’ll feel confident and comfortable.

How to Present a Research Paper

If your PowerPoint presentation is solid, and you’ve practiced your presentation, that’s half the battle. Follow the basic advice to keep your audience engaged and interested by making eye contact, encouraging questions, and presenting your information with enthusiasm.

We encourage you to read our articles on how to present a scientific journal article and tips on giving good scientific presentations .

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How to present a research paper in PPT: best practices

A research paper presentation is frequently used at conferences and other events where you have a chance to share the results of your research and receive feedback from colleagues. Although it may appear as simple as summarizing the findings, successful examples of research paper presentations show that there is a little bit more to it.

In this article, we’ll walk you through the basic outline and steps to create a good research paper presentation. We’ll also explain what to include and what not to include in your presentation of research paper and share some of the most effective tips you can use to take your slides to the next level.

Research paper PowerPoint presentation outline

Creating a PowerPoint presentation for a research paper involves organizing and summarizing your key findings, methodology, and conclusions in a way that encourages your audience to interact with your work and share their interest in it with others. Here’s a basic research paper outline PowerPoint you can follow:

1. Title (1 slide)

Typically, your title slide should contain the following information:

• Title of the research paper
• Affiliation or institution
• Date of presentation

2. Introduction (1-3 slides)

On this slide of your presentation, briefly introduce the research topic and its significance and state the research question or objective.

3. Research questions or hypothesis (1 slide)

This slide should emphasize the objectives of your research or present the hypothesis.

4. Literature review (1 slide)

Your literature review has to provide context for your research by summarizing relevant literature. Additionally, it should highlight gaps or areas where your research contributes.

5. Methodology and data collection (1-2 slides)

This slide of your research paper PowerPoint has to explain the research design, methods, and procedures. It must also Include details about participants, materials, and data collection and emphasize special equipment you have used in your work.

6. Results (3-5 slides)

On this slide, you must present the results of your data analysis and discuss any trends, patterns, or significant findings. Moreover, you should use charts, graphs, and tables to illustrate data and highlight something novel in your results (if applicable).

7. Conclusion (1 slide)

Your conclusion slide has to summarize the main findings and their implications, as well as discuss the broader impact of your research. Usually, a single statement is enough.

8. Recommendations (1 slide)

If applicable, provide recommendations for future research or actions on this slide.

9. References (1-2 slides)

The references slide is where you list all the sources cited in your research paper.

10. Acknowledgments (1 slide)

On this presentation slide, acknowledge any individuals, organizations, or funding sources that contributed to your research.

11. Appendix (1 slide)

If applicable, include any supplementary materials, such as additional data or detailed charts, in your appendix slide.

The above outline is just a general guideline, so make sure to adjust it based on your specific research paper and the time allotted for the presentation.

Steps to creating a memorable research paper presentation

Creating a PowerPoint presentation for a research paper involves several critical steps needed to convey your findings and engage your audience effectively, and these steps are as follows:

Step 1. Understand your audience:

• Identify the audience for your presentation.
• Tailor your content and level of detail to match the audience’s background and knowledge.

Step 2. Define your key messages:

• Clearly articulate the main messages or findings of your research.
• Identify the key points you want your audience to remember.

Step 3. Design your research paper PPT presentation:

• Use a clean and professional design that complements your research topic.
• Choose readable fonts, consistent formatting, and a limited color palette.
• Opt for PowerPoint presentation services if slide design is not your strong side.

Step 4. Put content on slides:

• Follow the outline above to structure your presentation effectively; include key sections and topics.
• Organize your content logically, following the flow of your research paper.

Step 5. Final check:

• Proofread your slides for typos, errors, and inconsistencies.
• Ensure all visuals are clear, high-quality, and properly labeled.

Step 6. Save and share:

• Save your presentation and ensure compatibility with the equipment you’ll be using.
• If necessary, share a copy of your presentation with the audience.

By following these steps, you can create a well-organized and visually appealing research paper presentation PowerPoint that effectively conveys your research findings to the audience.

What to include and what not to include in your presentation

In addition to the must-know PowerPoint presentation recommendations, which we’ll cover later in this article, consider the following do’s and don’ts when you’re putting together your research paper presentation:

• Focus on the topic.
• Be brief and to the point.
• Attract the audience’s attention and highlight interesting details.
• Use only relevant visuals (maps, charts, pictures, graphs, etc.).
• Use numbers and bullet points to structure the content.
• Make clear statements regarding the essence and results of your research.

Don’ts:

• Don’t write down the whole outline of your paper and nothing else.
• Don’t put long, full sentences on your slides; split them into smaller ones.
• Don’t use distracting patterns, colors, pictures, and other visuals on your slides; the simpler, the better.
• Don’t use too complicated graphs or charts; only the ones that are easy to understand.
• Now that we’ve discussed the basics, let’s move on to the top tips for making a powerful presentation of your research paper.

8 tips on how to make research paper presentation that achieves its goals

You’ve probably been to a presentation where the presenter reads word for word from their PowerPoint outline. Or where the presentation is cluttered, chaotic, or contains too much data. The simple tips below will help you summarize a 10 to 15-page paper for a 15 to 20-minute talk and succeed, so read on!

Tip #1: Less is more

You want to provide enough information to make your audience want to know more. Including details but not too many and avoiding technical jargon, formulas, and long sentences are always good ways to achieve this.

Tip #2: Be professional

Avoid using too many colors, font changes, distracting backgrounds, animations, etc. Bullet points with a few words to highlight the important information are preferable to lengthy paragraphs. Additionally, include slide numbers on all PowerPoint slides except for the title slide, and make sure it is followed by a table of contents, offering a brief overview of the entire research paper.

Tip #3: Strive for balance

PowerPoint slides have limited space, so use it carefully. Typically, one to two points per slide or 5 lines for 5 words in a sentence are enough to present your ideas.

Tip #4: Use proper fonts and text size

The font you use should be easy to read and consistent throughout the slides. You can go with Arial, Times New Roman, Calibri, or a combination of these three. An ideal text size is 32 points, while a heading size is 44.

Tip #5: Concentrate on the visual side

A PowerPoint presentation is one of the best tools for presenting information visually. Use graphs instead of tables and topic-relevant illustrations instead of walls of text. Keep your visuals as clean and professional as the content of your presentation.

Tip #6: Practice your delivery

Always go through your presentation when you’re done to ensure a smooth and confident delivery and time yourself to stay within the allotted limit.

Tip #7: Get ready for questions

Anticipate potential questions from your audience and prepare thoughtful responses. Also, be ready to engage in discussions about your research.

Tip #8: Don’t be afraid to utilize professional help

If the mere thought of designing a presentation overwhelms you or you’re pressed for time, consider leveraging professional PowerPoint redesign services . A dedicated design team can transform your content or old presentation into effective slides, ensuring your message is communicated clearly and captivates your audience. This way, you can focus on refining your delivery and preparing for the presentation.

Lastly, remember that even experienced presenters get nervous before delivering research paper PowerPoint presentations in front of the audience. You cannot know everything; some things can be beyond your control, which is completely fine. You are at the event not only to share what you know but also to learn from others. So, no matter what, dress appropriately, look straight into the audience’s eyes, try to speak and move naturally, present your information enthusiastically, and have fun!

If you need help with slide design, get in touch with our dedicated design team and let qualified professionals turn your research findings into a visually appealing, polished presentation that leaves a lasting impression on your audience. Our experienced designers specialize in creating engaging layouts, incorporating compelling graphics, and ensuring a cohesive visual narrative that complements content on any subject.

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How to Make an Effective Research Presentation

Presentation software programs have advanced to the point where you no longer need to be an experienced designer to put together a compelling piece of collateral that conveys your findings about academic research in exactly the right way. With the right materials, the right presentation software, and a little bit of time, you can visualize any data that you have in the form of a terrific presentation that sells your research better than numbers alone ever could. However, this does not mean that you shouldn’t keep in mind a few things. As both a marketing tool and a means to convey information, presentations are helpful because they are malleable—the format can essentially be anything you need it to be at any given time. The other side of this, however, is that there are certain traps that are all too easy for even experts to fall into that will harm your ultimate message, not help it. If you wish to learn how to make a professional research presentation as an author, or a researcher, then you should avoid some mistakes at all costs.

Mistakes to Avoid

As a researcher or a student, your number one goal isn’t just to provide insight into a topic—it’s to do so in a compelling way. It is important to communicate ideas in a way that is both easy to understand for people who haven’t completed the work you have and to do so in a compelling and engaging way. In many ways, it’s a lot like telling a story—albeit one that is heavily research-oriented. Every story has a beginning, middle, and end and you need to ensure that the content in the presentation has a proper narrative flow.

In many ways, your presentation will operate exactly along the same lines. To that end, always remember to make sure that the information is presented not only in the right manner but also in the right order to complement intent and maximize impact. If you have three subtopics within a presentation, all of which are related but are still different ideas, don’t mix and match the content. Don’t jump from one topic to the other and back again—you’re only going to lose focus and eventually, the attention of your reader.

If you start preparing your presentation and realize that you’re actually kind of covering two distinct and different topics, don’t be afraid to break one presentation into two. You’ll be able to devote more attention to promoting each idea and you’ll walk away with two great pieces of research presentations instead of one “okay” one.

Length of Your Presentation

Another element of your presentation that you need to pay extremely close attention to is the length. This goes back to another one of the old rules of storytelling: “Whatever you do, don’t overstay your welcome.” While it is true that presentations are naturally designed to be a longer form than something like an Infographic, it’s important to recognize when you’re asking too much of your reader/viewer. A presentation isn’t just a visualized form of something like a white paper. It’s a unique medium all unto itself.

When you start preparing your presentation for the first time, feel free to include as many slides or as much information as you want. Also, don’t forget that there are three versions of your presentation that will exist—the initial outline, the “first draft” of the presentation and the final edited version that you release. Make an effort to only include information that A) is needed to understand your research topic, and B) is necessary to contextualize your findings or the points you’re trying to make. Go through your presentation from start to finish and really try to experience it with fresh eyes—the same way your audience will.

Does it feel like the end of your presentation is getting a little sluggish? You feel that it should be over but there are ten slides to go still. Be precise in your editing process —rest assured that you’ll thank yourself when the end result is much more powerful than it would be if it had remained bloated.

The Power of Presentations

In many ways, presentations provide a unified experience where you can have text, images, video, and more. Remember that human beings are visual learners— visuals are processed up to 60,000 times faster than text and people have a much easier time understanding complex information when it is paired with relevant images as opposed to just text. As an author, researcher, or student, your job is to take complicated ideas and present them in a way that is appealing to a larger audience. Presentations are one of the most essential ways for you to do exactly that. The central message you are trying to convey—the thesis, if you will—needs to be strong enough to justify the creation of a presentation in the first place.

It needs to be a big enough topic to warrant a lengthy experience and a compelling enough story that demands to be told in this particular format above all others. If you start from that simple foundation and build outward, you’ll be left with the best type of marketing tool—one that promotes your research for you and one that people can’t wait to share with their friends and colleagues.

About the Author

Payman Taei is the founder of Visme , an easy-to-use online tool to create engaging presentations, infographics, and other forms of visual content. He is also the founder of HindSite Interactive , an award-winning Maryland based digital agency specializing in website design, user experience, and web app development.

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Academic writing has features that vary only slightly across the different disciplines. Knowing these elements and the purpose of each serves help you to read and understand academic texts efficiently and effectively, and then apply what you read to your paper or project.

Social Science (and Science) original research articles generally follow IMRD: Introduction- Methods-Results-Discussion

Introduction

• Introduces topic of article
• Presents the "Research Gap"/Statement of Problem article will address
• How research presented in the article will solve the problem presented in research gap.
• Literature Review. presenting and evaluating previous scholarship on a topic.  Sometimes, this is separate section of the article. 

​Method & Results

• How research was done, including analysis and measurements.  
• Sometimes labeled as "Research Design"
• What answers were found
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Body  

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• Discuss why others may disagree (counter-evidence) and why argument is still valid
• Summary of argument
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• Anticipate and address possible counter-claims
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Home Blog Presentation Ideas How to Create and Deliver a Research Presentation

How to Create and Deliver a Research Presentation

Every research endeavor ends up with the communication of its findings. Graduate-level research culminates in a thesis defense , while many academic and scientific disciplines are published in peer-reviewed journals. In a business context, PowerPoint research presentation is the default format for reporting the findings to stakeholders.

Condensing months of work into a few slides can prove to be challenging. It requires particular skills to create and deliver a research presentation that promotes informed decisions and drives long-term projects forward.

Table of Contents

What is a Research Presentation

Key slides for creating a research presentation, tips when delivering a research presentation, how to present sources in a research presentation, recommended templates to create a research presentation.

A research presentation is the communication of research findings, typically delivered to an audience of peers, colleagues, students, or professionals. In the academe, it is meant to showcase the importance of the research paper , state the findings and the analysis of those findings, and seek feedback that could further the research.

The presentation of research becomes even more critical in the business world as the insights derived from it are the basis of strategic decisions of organizations. Information from this type of report can aid companies in maximizing the sales and profit of their business. Major projects such as research and development (R&D) in a new field, the launch of a new product or service, or even corporate social responsibility (CSR) initiatives will require the presentation of research findings to prove their feasibility.

Market research and technical research are examples of business-type research presentations you will commonly encounter.

In this article, we’ve compiled all the essential tips, including some examples and templates, to get you started with creating and delivering a stellar research presentation tailored specifically for the business context.

Various research suggests that the average attention span of adults during presentations is around 20 minutes, with a notable drop in an engagement at the 10-minute mark . Beyond that, you might see your audience doing other things.

How can you avoid such a mistake? The answer lies in the adage “keep it simple, stupid” or KISS. We don’t mean dumbing down your content but rather presenting it in a way that is easily digestible and accessible to your audience. One way you can do this is by organizing your research presentation using a clear structure.

Here are the slides you should prioritize when creating your research presentation PowerPoint.

1.  Title Page

The title page is the first thing your audience will see during your presentation, so put extra effort into it to make an impression. Of course, writing presentation titles and title pages will vary depending on the type of presentation you are to deliver. In the case of a research presentation, you want a formal and academic-sounding one. It should include:

• The full title of the report
• The date of the report
• The name of the researchers or department in charge of the report
• The name of the organization for which the presentation is intended

When writing the title of your research presentation, it should reflect the topic and objective of the report. Focus only on the subject and avoid adding redundant phrases like “A research on” or “A study on.” However, you may use phrases like “Market Analysis” or “Feasibility Study” because they help identify the purpose of the presentation. Doing so also serves a long-term purpose for the filing and later retrieving of the document.

Here’s a sample title page for a hypothetical market research presentation from Gillette .

2. Executive Summary Slide

The executive summary marks the beginning of the body of the presentation, briefly summarizing the key discussion points of the research. Specifically, the summary may state the following:

• The purpose of the investigation and its significance within the organization’s goals
• The methods used for the investigation
• The major findings of the investigation
• The conclusions and recommendations after the investigation

Although the executive summary encompasses the entry of the research presentation, it should not dive into all the details of the work on which the findings, conclusions, and recommendations were based. Creating the executive summary requires a focus on clarity and brevity, especially when translating it to a PowerPoint document where space is limited.

Each point should be presented in a clear and visually engaging manner to capture the audience’s attention and set the stage for the rest of the presentation. Use visuals, bullet points, and minimal text to convey information efficiently.

3. Introduction/ Project Description Slides

In this section, your goal is to provide your audience with the information that will help them understand the details of the presentation. Provide a detailed description of the project, including its goals, objectives, scope, and methods for gathering and analyzing data.

You want to answer these fundamental questions:

• What specific questions are you trying to answer, problems you aim to solve, or opportunities you seek to explore?
• Why is this project important, and what prompted it?
• What are the boundaries of your research or initiative? 
• How were the data gathered?

Important: The introduction should exclude specific findings, conclusions, and recommendations.

4. Data Presentation and Analyses Slides

This is the longest section of a research presentation, as you’ll present the data you’ve gathered and provide a thorough analysis of that data to draw meaningful conclusions. The format and components of this section can vary widely, tailored to the specific nature of your research.

For example, if you are doing market research, you may include the market potential estimate, competitor analysis, and pricing analysis. These elements will help your organization determine the actual viability of a market opportunity.

Visual aids like charts, graphs, tables, and diagrams are potent tools to convey your key findings effectively. These materials may be numbered and sequenced (Figure 1, Figure 2, and so forth), accompanied by text to make sense of the insights.

5. Conclusions

The conclusion of a research presentation is where you pull together the ideas derived from your data presentation and analyses in light of the purpose of the research. For example, if the objective is to assess the market of a new product, the conclusion should determine the requirements of the market in question and tell whether there is a product-market fit.

Designing your conclusion slide should be straightforward and focused on conveying the key takeaways from your research. Keep the text concise and to the point. Present it in bullet points or numbered lists to make the content easily scannable.

6. Recommendations

The findings of your research might reveal elements that may not align with your initial vision or expectations. These deviations are addressed in the recommendations section of your presentation, which outlines the best course of action based on the result of the research.

What emerging markets should we target next? Do we need to rethink our pricing strategies? Which professionals should we hire for this special project? — these are some of the questions that may arise when coming up with this part of the research.

Recommendations may be combined with the conclusion, but presenting them separately to reinforce their urgency. In the end, the decision-makers in the organization or your clients will make the final call on whether to accept or decline the recommendations.

7. Questions Slide

Members of your audience are not involved in carrying out your research activity, which means there’s a lot they don’t know about its details. By offering an opportunity for questions, you can invite them to bridge that gap, seek clarification, and engage in a dialogue that enhances their understanding.

If your research is more business-oriented, facilitating a question and answer after your presentation becomes imperative as it’s your final appeal to encourage buy-in for your recommendations.

A simple “Ask us anything” slide can indicate that you are ready to accept questions.

1. Focus on the Most Important Findings

The truth about presenting research findings is that your audience doesn’t need to know everything. Instead, they should receive a distilled, clear, and meaningful overview that focuses on the most critical aspects.

You will likely have to squeeze in the oral presentation of your research into a 10 to 20-minute presentation, so you have to make the most out of the time given to you. In the presentation, don’t soak in the less important elements like historical backgrounds. Decision-makers might even ask you to skip these portions and focus on sharing the findings.

2. Do Not Read Word-per-word

Reading word-for-word from your presentation slides intensifies the danger of losing your audience’s interest. Its effect can be detrimental, especially if the purpose of your research presentation is to gain approval from the audience. So, how can you avoid this mistake?

• Make a conscious design decision to keep the text on your slides minimal. Your slides should serve as visual cues to guide your presentation.
• Structure your presentation as a narrative or story. Stories are more engaging and memorable than dry, factual information.
• Prepare speaker notes with the key points of your research. Glance at it when needed.
• Engage with the audience by maintaining eye contact and asking rhetorical questions.

3. Don’t Go Without Handouts

Handouts are paper copies of your presentation slides that you distribute to your audience. They typically contain the summary of your key points, but they may also provide supplementary information supporting data presented through tables and graphs.

The purpose of distributing presentation handouts is to easily retain the key points you presented as they become good references in the future. Distributing handouts in advance allows your audience to review the material and come prepared with questions or points for discussion during the presentation.

4. Actively Listen

An equally important skill that a presenter must possess aside from speaking is the ability to listen. We are not just talking about listening to what the audience is saying but also considering their reactions and nonverbal cues. If you sense disinterest or confusion, you can adapt your approach on the fly to re-engage them.

For example, if some members of your audience are exchanging glances, they may be skeptical of the research findings you are presenting. This is the best time to reassure them of the validity of your data and provide a concise overview of how it came to be. You may also encourage them to seek clarification.

5. Be Confident

Anxiety can strike before a presentation – it’s a common reaction whenever someone has to speak in front of others. If you can’t eliminate your stress, try to manage it.

People hate public speaking not because they simply hate it. Most of the time, it arises from one’s belief in themselves. You don’t have to take our word for it. Take Maslow’s theory that says a threat to one’s self-esteem is a source of distress among an individual.

Now, how can you master this feeling? You’ve spent a lot of time on your research, so there is no question about your topic knowledge. Perhaps you just need to rehearse your research presentation. If you know what you will say and how to say it, you will gain confidence in presenting your work.

All sources you use in creating your research presentation should be given proper credit. The APA Style is the most widely used citation style in formal research.

In-text citation

Add references within the text of your presentation slide by giving the author’s last name, year of publication, and page number (if applicable) in parentheses after direct quotations or paraphrased materials. As in:

The alarming rate at which global temperatures rise directly impacts biodiversity (Smith, 2020, p. 27).

If the author’s name and year of publication are mentioned in the text, add only the page number in parentheses after the quotations or paraphrased materials. As in:

According to Smith (2020), the alarming rate at which global temperatures rise directly impacts biodiversity (p. 27).

Image citation

All images from the web, including photos, graphs, and tables, used in your slides should be credited using the format below.

Creator’s Last Name, First Name. “Title of Image.” Website Name, Day Mo. Year, URL. Accessed Day Mo. Year.

Work cited page

A work cited page or reference list should follow after the last slide of your presentation. The list should be alphabetized by the author’s last name and initials followed by the year of publication, the title of the book or article, the place of publication, and the publisher. As in:

Smith, J. A. (2020). Climate Change and Biodiversity: A Comprehensive Study. New York, NY: ABC Publications.

When citing a document from a website, add the source URL after the title of the book or article instead of the place of publication and the publisher. As in:

Smith, J. A. (2020). Climate Change and Biodiversity: A Comprehensive Study. Retrieved from https://www.smith.com/climate-change-and-biodiversity.

1. Research Project Presentation PowerPoint Template

A slide deck containing 18 different slides intended to take off the weight of how to make a research presentation. With tons of visual aids, presenters can reference existing research on similar projects to this one – or link another research presentation example – provide an accurate data analysis, disclose the methodology used, and much more.

Use This Template

2. Research Presentation Scientific Method Diagram PowerPoint Template

Whenever you intend to raise questions, expose the methodology you used for your research, or even suggest a scientific method approach for future analysis, this circular wheel diagram is a perfect fit for any presentation study.

Customize all of its elements to suit the demands of your presentation in just minutes.

3. Thesis Research Presentation PowerPoint Template

If your research presentation project belongs to academia, then this is the slide deck to pair that presentation. With a formal aesthetic and minimalistic style, this research presentation template focuses only on exposing your information as clearly as possible.

Use its included bar charts and graphs to introduce data, change the background of each slide to suit the topic of your presentation, and customize each of its elements to meet the requirements of your project with ease.

4. Animated Research Cards PowerPoint Template

Visualize ideas and their connection points with the help of this research card template for PowerPoint. This slide deck, for example, can help speakers talk about alternative concepts to what they are currently managing and its possible outcomes, among different other usages this versatile PPT template has. Zoom Animation effects make a smooth transition between cards (or ideas).

5. Research Presentation Slide Deck for PowerPoint

With a distinctive professional style, this research presentation PPT template helps business professionals and academics alike to introduce the findings of their work to team members or investors.

By accessing this template, you get the following slides:

• Introduction
• Problem Statement
• Research Questions
• Conceptual Research Framework (Concepts, Theories, Actors, & Constructs)
• Study design and methods
• Population & Sampling
• Data Collection
• Data Analysis

Check it out today and craft a powerful research presentation out of it!

A successful research presentation in business is not just about presenting data; it’s about persuasion to take meaningful action. It’s the bridge that connects your research efforts to the strategic initiatives of your organization. To embark on this journey successfully, planning your presentation thoroughly is paramount, from designing your PowerPoint to the delivery.

Take a look and get inspiration from the sample research presentation slides above, put our tips to heart, and transform your research findings into a compelling call to action.

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Article Sections

• Section 1: When Are Statistics Needed and What Is the Purpose of Statistics in a Research Paper?
• What goes in the Materials and Methods section?
• What goes in the Results section?
• Additional details and descriptions about design, data collection, and analysis
• Pointers for Writing about Statistics for the Horticultural Sciences

Related Content

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• Next Article

Best Practices for Presenting Statistical Information in a Research Article

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A key characteristic of scientific research is that the entire experiment (or series of experiments), including the data analyses, is reproducible. This aspect of science is increasingly emphasized. The Materials and Methods section of a scientific paper typically contains the necessary information for the research to be replicated and expanded on by other scientists. Important components are descriptions of the study design, data collection, and statistical analysis of those data, including the software used. In the Results section, statistical analyses are presented; these are usually best absorbed from figures. Model parameter estimates (including variances) and effect sizes should also be included in this section, not just results of significance tests, because they are needed for subsequent power and meta-analyses. In this article, we give key components to include in the descriptions of study design and analysis, and discuss data interpretation and presentation with examples from the horticultural sciences.

This article provides recommendations for statistical reporting in a research journal article. Appropriate and informative reporting, and the wise use of statistical design and analysis throughout the research process, are both essential to good science; neither can happen without the other. In addition, many journals now require access to original data and the code used for analyses. This article is not a statistics tutorial; we do not explain how to do any of the statistical methods mentioned. There are many, many papers and books that provide that information; some are cited in our reference and selected reading section. Instead, we give guidelines for horticultural scientists on how best to incorporate and present statistical information in a scientific paper. We also focus on experimental rather than observational studies. To do the latter justice would require greatly expanding this article, and the majority of papers published by the American Society for Horticultural Scientists are experimental studies. A very useful complementary article is by Onofri et al. (2010) , which gives specific advice for many issues we treat only generally.

This paper is divided into two sections, as follows:

Section 1. When Are Statistics Needed and What Is the Purpose of Statistics in a Research Paper?

Section 2. Recommendations for Writing about Statistics in a Research Paper

What Goes in the Materials and Methods Section?

What Goes in the Results Section?

Additional Details and Descriptions about Design, Data Collection, and Analysis

Literature Cited and Selected References

The scope of horticultural research is large and not all studies require statistics. For example, anatomical and morphological studies can be purely descriptive. With that said, these kinds of descriptive studies are a subset of observational studies, which also include studies at the genomic, ecologic, and landscape level. For observational studies, there are useful methods for determining associations, clusters, and dimension reduction, to name a few, that are statistics based. In this article we focus primarily on research questions that require inferential statistics. Typically, using designed experiments when addressing a research question requires experiment planning, data collection, and subsequent statistical analysis, and the following recommendations are applicable.

The statistical section in an article serves five general functions. First, the design, data collection, method of analysis, and software used must be described with sufficient clarity to demonstrate that the study is capable of addressing the primary objectives of the research. When adequate information is provided, it allows for an informed peer review and for readers, in principle, to reproduce the study, including the data analysis . Second, authors must provide sufficient documentation to create confidence that the data have been analyzed appropriately. This includes verifying required statistical assumptions and justifying choices—such as the chosen mean comparison procedure and any other method that might affect results and conclusions, such as controlling for experimental-wise error. Experiment-wise error rate (or family-wise error rate, depending on how family is defined) is the probability of committing at least one Type I error throughout the whole experiment. Although the error rate for an individual hypothesis test may be small, if one tests many hypotheses, one becomes more likely to declare false significance for at least one. If the tests are not independent (e.g., using the same plants to test multiple attributes or over time, as is common in this field), this can increase the experiment-wise error rate. For example, if a plant in one treatment group is diseased, this will affect all the (correlated) measures of that group, and thus all hypotheses tests. Third, data and their analyses must be presented coherently. The statistical model and analysis should naturally follow from the study design, and be consistent with relevant characteristics of the data, such as the underlying sampling distribution (e.g., normal, Poisson, binomial). Figures and tables should illustrate, and be consistent with, important results from the analysis. Fourth, readers should not have to guess which scientific questions the analysis answers. Effects deemed statistically significant must also be shown to be biologically/economically important. Effects of potential biologic/economic importance but whose statistical significance is not supported by the data should also be reported. There is an implicit assumption of adequate power when discussing results from any statistical tests. Power is estimated during the design phase using results from previous experiments or parameter estimates from the literature. Fifth, readers should be able to use information in the statistical reporting section as a resource for planning future experiments. Variance estimates are especially important for this function.

The goal of this article is to provide an overview of how best to communicate statistics used in horticultural research. Therefore, it does not include specifics to address every contingency. Statistical methods continuously change, with new methods developed to address advances in biologic and ecologic research. For many studies, traditional and familiar methods (a.k.a. “standard statistics”) are adequate. However, for other studies, newer, less familiar methods are preferable, if not essential. Use of newer methods should not be an obstacle for publication.

Section 2: Recommendations for Writing about Statistics in a Research Paper

The following sections outline key points that should be addressed in the Materials and Methods section, and in the Results section of a journal article. Kramer et al. (2016) document common statistical problems for a sample of horticultural articles and should be used as a checklist of mistakes to avoid. The work by Reinhart (2015) is not overly technical and it explains many of these issues and other mistakes further, mostly in a biologic context.

Broadly speaking, there are two main statistical areas that the Materials and Methods section should address: 1) how was the study designed and 2) how were the data analyzed. Recommendations are grouped by subtopic.

Design and data collection.

The main idea of this section is to provide all information relevant to subsequent statistical analysis and interpretation about the design—specifically, how the experiment was conducted, how the data were collected and subsequently handled up to the point when the data were ready for statistical analysis. These are detailed next.

Describe the design. There are two components of experimental design: the experiment design and the treatment design. Both must be described.

The treatment design refers to the organization of treatment factors. Factorial designs (e.g., varieties × potting substrate) and dose–response (e.g., amount of nutrient applied) are familiar examples.

The experiment design refers to how the experimental units were organized and how randomization was done. Familiar examples are the completely randomized design (CRD) and randomized complete block design (RCBD). Any restrictions on randomization (e.g., blocking) or other ways observations were grouped must be described; this is part of the experiment design.

Describe covariates, if any. Provide the units of replication (the experimental unit; in other words, the smallest unit to which treatments were assigned independently) and the units of observation (sampling unit). The units of replication may differ for different factors (as they do, for example, in a split-plot design).

Describe how data were collected and how samples were pooled/batched, if this was done. Identify whether these were one-time measurements, multiple measurements on the plant/plot at the same time, repeated measures over time, or measurements on different plant characteristics.

Provide numbers, so it is clear how many units were in each block/group, how many received each treatment, and so on. Total sample size must be easily calculated, if not given. If a power analysis was used to determine the sample size, provide details. If not, explain how the sample size was determined. For example, one could write: “Growth chambers were limited to 30 plants, and three growth chambers were available. Previous studies using a similar setup and similar plant numbers had no difficulty detecting even moderate differences in growth patterns.”

Identify which variables are dependent (i.e., the response variables one measures, such as yield, biomass, time to flowering, elemental concentration) and which are independent (see the previous description of treatment design).

Describe any transformation of variables (e.g., logarithmic transformation) and the reason it was needed; this applies to both dependent and independent variables. Often, dependent variables can be fit without transformation if the appropriate sampling distribution is specified in a generalized linear model. When this is possible, generalized linear models are preferable to variance stabilizing transformations.

Data analysis.

Broadly speaking, data analysis includes the following steps:

Plot the original data to visualize what has happened in terms of treatment effects, distribution of data, and other features of the data deemed to be important.

Determine a statistical model consistent with the study design and the distribution of the data, and mean comparison procedures needed to address the objective of the research.

Determine the statistical assumptions associated with the selected model.

Select the software to be used to implement the analysis.

Run the analysis and verify that the assumptions are satisfied.

Report in the Materials and Methods section how the previous steps were completed.

Report the outcome of the analysis in the Results section.

There is no one-size-fits-all way of presenting the results of a statistical analysis. This is true for many aspects of using statistics in horticultural science, making it impossible to give advice covering every situation; instead, we provide general guidelines. Authors must decide what best tells the story of their research results. Tables and figures are common methods of presenting data results. The following are principles to follow:

If you include graphics showing the data, presenting data summaries, or depicting results from modeling, the intent is to portray the findings of the research accurately and make it easier for readers to visually understand the data, estimates and findings from the analysis.

Statistics that appear in both figures and tables should be consistent with the way the data were analyzed. If objectives are addressed using descriptive statistics, then these should appear in a figure or table, along with their appropriate measures of variability.

If the objectives are addressed using a statistical model, as is usually the case, then statistics obtained from the model should appear in the figure or table, along with their appropriate measures of variability.

For modeling results and hypothesis testing, there are two main categories of output from statistical software that should be presented: 1) diagnostic information demonstrating that the method and statistical model used are appropriate and 2) parameter estimates and hypothesis tests that bear directly on the research objectives. The connection to the research objectives must be clear for each statistical result (do not simply copy results produced by software). Two other categories of statistical results should be considered: 1) estimates of quantities from the model that may be useful in future research (e.g., variance estimates) and 2) statistical support for unexpected findings.

Demonstrate that model assumptions were satisfied (this could be just a sentence for simple models). See the previous point.

For multiple dependent variables, give the correlations among these variables [and possibly the correlations separately for each treatment if the treatments affect the correlations (discussed later)]. Experiment-wise error control may be necessary.

Statistics for the Materials and Methods section.

The Materials and Methods section should address the first function given in Section 1. The design, data collection, method of analysis, and software used must be described clearly. When choices were made or when nonstandard procedures were used must be justified.

Description of the study design.

This means “design” as broadly defined. If data were collected, whether from an observational study, a survey, or a designed experiment, there was a design. At a minimum, all designs include three elements: The first is the response variable (i.e., the outcome or outcomes measured), the second is the treatment design (i.e., the treatments or conditions being evaluated), and the third is the design structure of the experiment, which includes the units of replication (called the experimental unit in designed experiments), the units of observation (called the sampling unit in designed experiments), and grouping of units, if any. Grouping may consist of blocking, research conducted at multiple locations, or data collected on multiple occasions.

The following are three scenarios to illustrate these points. Scenario 1: Suppose there are plants in flats on a bench. If treatments are assigned randomly and applied to the bench, the bench is the experimental unit. If observations are made on the flat, then the flat is the unit of observation (sampling unit). This is a CRD. Scenario 2: If treatments are assigned randomly to individual flats within each bench, then flat is the experimental unit. Bench is a blocking factor. If observations are made on the flat, then the flat is the unit of observation. Notice that the experimental unit and the sampling unit can be identical. This is not the case in scenario 1. This is an RCBD. Scenario 3: Experiments with factorial treatment designs often have different-size experimental units for different factors. In this scenario, irrigation or nutrients are applied using drip lines across a bench, but each bench has several flats, with a different variety in each flat. Here, bench is the experimental unit with respect to irrigation/nutrient and flat is the experimental unit with respect to variety. In design language, this is a split-plot experiment, with the bench as the whole-plot experimental unit, irrigation/nutrient is the whole-plot treatment factor, flat is the split-plot experimental unit, and variety is the split-plot treatment factor. See Onofri et al. (2010) for another good example illustrating true and pseudo-replication.

Important note: Although it is acceptable to name the design, such as an RCBD or Latin square design, a name alone is insufficient and may be misleading. So regardless of whether a design name is used, authors must give the treatment factors, the experimental units, sampling units, and the blocking criteria (if any). For example, an RCBD may or may not have treatments replicated in each block. If treatments are replicated, one can test whether a treatment effect is the same in all blocks; if not, one has to assume it is. So, “RCBD” does not contain all the necessary information about the design.

Data collection.

This means list the response variables measured and describe how each was measured. It is also beneficial to make various plots of the original data to determine if there is a treatment effect (these plots are not necessarily included in the published paper). The biology should lead the statistics. Beyond this, you are looking for two things. When you describe the response variable, you want to focus on the sampling distribution of the response variable because this affects the model selected for the analysis of the data. You should plot the response variable against the predictor variables and look for recognizable patterns—in particular, to determine if (and how) variability changes systematically with the mean. For example, these may be scatterplots or boxplots. Another useful plot groups observations in a natural way (say, by treatment combination) and plots the means of the groups against their standard deviations. Many statistical methods assume the response variable is normally distributed, in which case variability should be roughly the same throughout the range of the response variable. A histogram of the residuals from the appropriate model with a normally distributed response variable results in a bell-shaped distribution. Note that a histogram of the raw response variable should not have a bell-shaped distribution because, if there really are treatment effects, the histogram should have a peak at each treatment mean.

Many commonly measured response variables in horticulture have a non-normal distribution. For example, germination rate (number of seeds germinated successfully/the number planted) has a binomial distribution. Many variables are continuous but have strongly right-skewed distributions, such as berry weight. A log-normal distribution often works well for this response variable. Generalized linear models allow the data to arise from many processes; the normal distribution is just one of several. Others include the log-normal, gamma, exponential, beta, binomial, Poisson, and negative binomial. The latter three are used to model count data. Again, plots used to assess the data and suggest models are part of your toolbox for determining the formal statistical analysis you will conduct, but usually are not included in an article.

The second thing you are looking for is any aspect of the data collection process that might affect the structure of the experiment design. Milliken and Johnson (2009) give examples in which the data collection process alters the study design. In one example, plants were grown in multiple distinct blocks, but then material for each treatment was combined from all blocks to allow measurement of the micronutrients of interest. The original blocks were legitimate replicates, but combining material precludes estimating block-to-block variability, effectively creating an unreplicated experiment. For this reason, a clear description of the data collection process is essential.

Model description.

Model description consists of giving the assumed distribution of the response variable and the sources of variation in the treatment and experiment design.

Scenario 1: plants assigned to benches in a CRD. The model would simply be Response = Treatment + Experimental error. (Plant-to-plant variability should be the largest contributor to the experimental error component.)

Scenario 2: treatments assigned to flats in an RCBD, with benches as the blocking criteria. The model would be Response = Treatment + Benches + Experimental error. This model assumes the treatment effect does not differ from bench to bench.

Scenario 3: Irrigation is the whole-plot treatment factor, benches are the whole-plot experimental units, variety is the split-plot treatment factor, and flat is the split-plot experimental unit. The model is Response = Irrigation treatment + Whole-plot error + Variety + Irrigation × Variety + Split-plot error. This model assumes the irrigation effect does not differ from bench to bench and that the variety effect does not differ from flat to flat. [In statistical jargon, there is no interaction between any of the fixed effects (irrigation and variety) and any of the random effects (bench and flat)].

Other aspects of analysis.

Because of the wide range of research subject matter and scales (laboratory to field), we give general principles. First, the statistical software used to analyze the data is not the method of analysis. Authors must first describe clearly the statistical procedures to compare or otherwise characterize the treatments. As illustrated in the three previous scenarios, the method of analysis must be consistent with the study design and data collection process. If there are assumptions critical to the validity of the method of analysis used, authors must state that the assumptions were met and how those assumptions were verified. If it is unclear what the assumptions are or how to verify them, talk to a statistician. Third, there must be a clear connection between the statistical methods used and the primary objectives of the research. This is where treatment design comes in, and it is important to match how you compare the treatments with the treatment design. For example, if you are comparing different varieties, then a mean comparison test is appropriate. Depending on the relative seriousness of Type I (false positives) and Type II (false negatives) errors, there are different ways to implement a means comparison test. At one extreme are two tests: Duncan multiple ranges test and an unprotected least significant difference test, neither of which control Type I error. At the other extreme are Scheffé and Bonferroni tests, which offer extreme control of Type I error at the expense of Type II error. There is a time and place for each test. Authors must state which procedure was used and why that procedure was chosen. The treatment design for experiments yielding genomic data is often simple, but the analyses are complicated. When analyzing RNAseq and similar genomic data, controlling for false discovery rate (which is also a multiple-comparisons issue) is similarly important.

In addition to factorial treatment designs [when main effects (factors with discrete levels) and their interactions are important], regression (when one or more predictor variables are continuous) is often used in horticulture. In some cases, continuous predictor variables are observational in nature. They are often called covariates in designs that also have factors. The distribution of the response variable needs to be stated because that distribution, in part, determines which statistical model is appropriate.

When the assumptions underlying a parametric method are violated, “nonparametric” methods should be used. These are not assumption-free; one assumption is that the response variable has the same sampling distribution across treatments (e.g., always skewed to the right).

Ratios constructed of two random variables (e.g., root mass/aboveground mass) have poor statistical properties (the assumptions of a parametric test are often violated because the variance of the ratios is not well determined). If ratios need to be used in an analysis, consider obtaining advice from a statistician familiar with the analysis of ratio data.

The trend in biological, medical, and social sciences journals is also to report effect sizes rather than simply the results of a significance test [see Nakagawa and Cuthill (2007) for a readable justification and concrete suggestions]. This now required in many journals ( Tressoldi et al., 2013 ).

With software improvements, Bayesian statistical methodology is gaining acceptance among biologists. In certain cases, such as models with layers of random effects, Bayesian methods enable analyses that would otherwise not be possible. In simpler models, there is often not much difference between results from Bayesian and frequentist (“traditional”) statistical analyses unless there is relevant prior information that improves the accuracy and precision of parameter estimates. Findings based on the use of Bayesian methodology are, in principle, acceptable in most biological journals, although require more explanation for readers to understand the results.

It may not be clear at the onset of an analysis which statistical methodology should be used, and several different kinds of analyses may be done with the same data set to determine which one makes the most sense. For example, diagnostics following fitting a model may suggest that the assumptions are not met. Alternative models may be examined to determine whether they fit the data better. This is not a free pass to try models until one finds the results one desires. Rather, one oscillates between fitting models and judging them using diagnostics until one is satisfied that one has selected a model that both captures the essential features of the data and has its assumptions satisfied. A useful discussion on obvious and not-so-obvious biases resulting from such a path is given by Gelman and Loken (2014) . Note that if two reasonable statistical models give contradictory conclusions, authors could present both, as long as sufficient information for the reviewers and readers to understand the issue is provided.

Statistical software.

After authors have described the method of analysis, following guidelines given previously, then any software used for statistical analyses should be cited, including online software. Include the version (the release) in the citation. Software developed by the authors for the analysis and, thus, not generally available should be explained sufficiently (perhaps in an appendix) for readers to understand what it does and why off-the-shelf software was not suitable. Authors must make the software available for others to use upon request and should include well-documented copies of the code for the reviewers. If the software was part of a system, such as SAS ® or R, authors must also give the specific procedure used, such SAS PROC GLIMMIX or the lme4 package in R.

Statistics for the Results section.

As with the method of analysis, there is no one-size-fits-all rule for presentation of data and associated formal statistical analysis. Again, we provide general principles.

First, data should be presented so that the relevant information with regard to the study’s primary objectives and most important findings are clear. Presentation may be via figures or tables, as long as these figures or tables inform rather than inadvertently hide or distort important information. In general, a picture is worth a thousand numbers. Well-conceived figures tend to portray the data’s important messages more understandably than tables.

If multiple responses are measured on the same sampling unit, such as weight, height, sugar content, and macro- and micronutrient content in a plant, correlation among these variables is likely and should be accounted for in the analysis (this is a kind of repeated-measures design) and correlation coefficients should be provided. Note that these correlations may change with different treatments or environments, just as mean responses may, so a single set of correlation coefficients may not summarize adequately the relationships among the variables in the experiment. If multiple responses are measured, experiment-wise error control may be needed. The same considerations for balancing Type I and Type II error rates could be applied here, as mentioned earlier.

Anytime means are compared, the standard error of the difference must be reported. In most cases, the standard error of a mean can be considered optional. This is admittedly a break with tradition, but it is an essential one. A plot depicting means with standard error bars is, by itself, insufficient.

Formal statistics.

Formal statistics include results of hypothesis tests (e.g., F or t statistics, P values), results of mean separation tests, estimates of means, differences, regression coefficients and their associated standard errors or confidence intervals, predicted values and their associated prediction intervals, and so on. In general, providing the mean (or mean difference) and its confidence interval is preferable to reporting only the results of a hypothesis test. Formal statistics should accompany and provide support for, but not substitute for, the depictions of the data described earlier. The American Statistical Association issued a policy statement in 2016 ( Wasserstein and Lazar, 2016 ) that clarifies legitimate vs. illegitimate uses and interpretations of P values associated with hypothesis tests. P values tell us whether the observed differences in the data are likely the result of chance or whether there is strong evidence of a true difference. They cannot tell us whether the difference is big enough to matter.

The main message should be that the observed difference is biologically, economically, or scientifically consequential, not that a P value was statistically significant. If the treatment group differs significantly from the control group, the emphasis should be on the biological consequences of finding a difference of that magnitude. If a regression line has a significant slope, the emphasis should be on the functional relationship between the independent and dependent variables. What underlying biological principle is responsible for a slope of this size? Let biology lead and let significance tests follow.

Often, not finding a statistically significant difference is important and should be reported if there was sufficient power to detect a biologically important difference. For example, if a study is done on the assumption (perhaps based on conventional wisdom or a previous research report) that a treatment difference exists, and data from a new study suggest otherwise, that information should be reported. Journals do science a major disservice by preferentially reporting only statistically significant results. This practice is called “publication bias” and is increasingly recognized to be a serious issue in all sciences. Sometimes a nondifference is the most important finding.

Many terms have technical meanings in statistics, as well as more general—and less precise—uses in common language. For example, “significant” has a specific definition in hypothesis testing, but the words “significant” and “important” tend to be used loosely and interchangeably when describing scientific results. It is best to avoid ambiguities in your writing (What is the meaning of “significant findings?”) Instead, describe the difference. For example, for a dry weight measurement, treatment A resulted in a heavier plant than treatment B. Commonly used statistical terms (e.g., analysis of variance) do not need to be defined in the article. Less common ones (e.g., reliability) do need accompanying definitions. If a reference needs to be given for a statistical technique, refer to an easily available (and commonly used) textbook if possible. The second choice would be an article in a horticulture or other biological journal. The third choice is a review article that explains the technique and perhaps compares it with others. The last choice is an article in the statistical literature that requires an advanced background in statistical theory.

Readers of an article may have a different reason for looking at results than the author’s stated purpose (e.g., to compare some of the results in the article with data readers have from a location they use, rather than the within-location comparison of cultivars in the article), which is another reason why summary information about the original data (e.g., means and standard deviations) needs to be provided. Data summaries may also be used in a subsequent meta-analysis; these typically use means, standard deviations, and other estimated parameters (e.g., block-to-block variance).

Statistics, and figures and tables.

Scientific publications are replete with tables, figures, and plots that are easy to read, technically impressive, pretty to look at, but, unfortunately, can be misleading in their content with respect to the objectives of the research they are intended to portray. If a figure shows the results of statistical modeling (e.g., means and their standard errors), you should try including the original data in the figure, perhaps in the background. This helps readers assess the adequacy of the statistical model visually. Rather than reiterate the advice of others, we suggest an excellent source for describing how data (and legends) should be presented: How to Report Statistics in Medicine ( Lang and Secic, 2006; pp. 325–393).

Plant scientists are not expected to know everything when conducting research, and this is becoming more evident with increasing collaborations across fields of study. Plant scientists should know, however, when they need input from a statistician. If so, we advise meeting with a statistician before setting up the experiment. A statistician will not be able to help after data from a poorly designed experiment are collected (other than to suggest rerunning the experiment with a better design).

A well-designed experiment can often be analyzed a number of ways and, usually, there are choices to make along the way. Examples include whether there is overdispersion, whether interaction terms are necessary, or whether a multivariate analysis should be considered to account for correlation among response variables. Should the statistician be extensively involved in the design and analysis, they should be included on the grant and/or the resulting journal article.

The following references are excellent sources for additional information about the statistical topics described in this article.

Bolker, B.M. , Brooks, M.E. , Clark, C.J. , Geange, S.W. , Poulsen, J.R. , Stevens, M.H. & White, J.S. 2009 Generalized linear mixed models: A practical guide for ecology and evolution Trends Ecol. Evol. 24 127 135

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Cochran, W.G. & Cox, G.M. 1957 Experimental designs. 2nd ed. Wiley, New York, NY

Cohen, J. 1992 A power primer Psychol. Bull. 112 155 159

Gelman, A. & Loken, E. 2014 The statistical crisis in science: Data-dependent analysis—a “garden of forking paths”—explains why many statistically significant comparisons don’t hold up Amer. Sci. 102 460

James, G. , Witten, D. , Hastie, T. & Tibshirani, R. 2013 An introduction to statistical learning. Springer, New York, NY

Keselman, H.J. 2015 Per family or familywise Type I error control: “Eether, eyether, neether, nyther, let’s call the whole thing off!” J. Mod. Appl. Stat. Methods 14 1 6

Kramer, M.H. , Paparozzi, E.T. & Stroup, W.W. 2016 Statistics in a horticultural journal: Problems and solutions J. Amer. Hort. Sci. 141 400 406

Lang, T.A. & . Secic, M 2006 How to report statistics in medicine: Annotated guidelines for authors, editors and reviewers. 2nd ed. American College of Physicians. Sheridan Press, Chelesa, MI

Little, T.M. 1978 If Galileo published in HortScience HortScience 13 504 506

Milliken, G.A. & Johnson, D.E. 2009 Analysis of messy data. Vol. 1, 2nd ed. Chapman & Hall/CRC Press, Boca Raton, FL

Nakagawa, S. & Cuthill, I.C. 2007 Effect size, confidence interval and statistical significance: A practical guide for biologists Biol. Rev. Camb. Philos. Soc. 82 591 605

Onofri, A. , Carbonell, E.A. , Piepho, H.-P. , Mortimer, A.M. & Cousens, R.D. 2010 Current statistical issues in Weed Research Weed Res. 50 524

Reinhart, A. 2015 Statistics done wrong: The woefully complete guide. No Starch Press, San Francisco, CA

Schabenberger, O. & Pierce, F.J. 2002 Contemporary statistical models for the plant and soil sciences. CRC Press, Boca Raton, FL

Stroup, W.W. 2013 Generalized linear mixed models: Modern concepts, methods and applications. CRC Press, Boca Raton, FL

Stroup, W.W. 2015 Rethinking the analysis of non-normal data in plant and soil science Agron. J. 107 811 827

Tressoldi, P.E. , Giofré, D. , Sella, F. & Cumming, G. 2013 High impact = high statistical standards? Not necessarily so PLoS One 8 2 E56180 doi: 10.1371/journal.pone.0056180

Vance, E.A. 2015 Recent developments and their implications for the future of academic statistical consulting centers Amer. Stat. 69 127 137

Wasserstein, R.L. & Lazar, N.A. 2016 The ASA’s statement on p -values: Context process, and purpose Amer. Stat. 70 129 133

Weissgerber, T.L. , Milic, N.M. , Winham, S.J. & Garovic, V.D. 2015 Beyond bar and line graphs: Time for a new data presentation paradigm PLoS Biol. 13 4 E1002128 doi:10.1371/journal.pbio.1002128

Contributor Notes

We thank the reviewers for their excellent comments and reference recommendations.

1 Corresponding author. E-mail: [email protected] .

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Beginner’s Guide to Research

Click here to download a .pdf copy of our Beginner’s Guide to Research !

Last updated : July 18, 2024

Consider keeping a printed copy to have when writing and revising your resume!  If you have any additional questions, make an appointment or email us at [email protected] !

Most professors will require the use of academic (AKA peer-reviewed) sources for student writing. This is because these sources, written for academic audiences of specific fields, are helpful for developing your argument on many topics of interest in the academic realm, from history to biology. While popular sources like news articles also often discuss topics of interest within academic fields, peer-reviewed sources offer a depth of research and expertise that you cannot find in popular sources. Therefore, knowing how to (1) identify popular vs. academic sources, (2) differentiate between primary and secondary sources, and (3) find academic sources is a vital step in writing research. Below are definitions of the two ways scholars categorize types of sources based on when they were created (i.e. time and place) and how (i.e. methodology):

Popular vs. academic sources:

• Popular sources are publicly accessible periodicals–newspapers, magazines, and blogs–such as The Washington Post or The New Yorker . These sources are most often written for non-academic audiences, but can be helpful for finding general information and a variety of opinions on your topic.
• Academic sources , known also as peer reviewed or scholarly articles, are those that have undergone peer review before being published. Typically, these articles are written for other scholars in the field and are published in academic journals, like Feminist Studies or The American Journal of Psychology . Literature reviews, research projects, case studies, and notes from the field are common examples.

Primary vs. secondary sources:

• Primary sources are articles written by people directly involved in what they were writing about, including: News reports and photographs, diaries and novels, films and videos, speeches and autobiographies, as well as original research and statistics.
• Secondary sources , on the other hand, are second hand accounts written about a topic based on primary sources. Whether a journal article or other academic publication is considered a secondary source depends on how you use it.

How to Find Academic Sources

Finding appropriate academic sources from the hundreds of different journal publications can be daunting. Therefore, it is important to find databases –digital collections of articles–relevant to your topic to narrow your search. Albertson’s Library has access to several different databases, which can be located by clicking the “Articles and Databases” tab on the website’s homepage, and navigating to “Databases A-Z” to refine your search. Popular databases include: Academic Search Premier and Proquest Central (non-specific databases which include a wide variety of articles), JSTOR (humanities and social sciences, from literature to history), Web of Science (formal sciences and natural sciences such as biology and chemistry), and Google Scholar (a web search engine that searches scholarly literature and academic sources). If you are unable to access articles from other databases, make sure you’re signed in to Alberton’s Library through Boise State!

Performing a Database Search

Databases include many different types of sources besides academic journals, however, including book reviews and other periodicals. Using the search bar , you can limit search results to those containing specific keywords or phrases like “writing center” or “transfer theory.” Utilizing keywords in your search–names of key concepts, authors, or ideas–rather than questions is the most effective way to find articles in databases. When searching for a specific work by title, placing the title in quotation marks will ensure your search includes only results in that specific word order. In the example below, search terms including the author (“Virginia Woolf”) and subject (“feminism”) are entered into the popular database EBSCOhost:

Refining Your Search Results

Many databases have a bar on the left of the screen where you can further refine your results. For example, if you are only interested in finding complete scholarly articles, or peer-reviewed ones, you can toggle these different options to further limit your search. These options are located under the “Refine Results” bar in EBSCOhost, divided into different sections, with a display of currently selected search filters and filter options to refine your search based on your specific needs, as seen in the figure below:

Search results can also be limited by subject : If you search “Romeo and Juliet” on Academic Search Premier to find literary analysis articles for your English class, you’ll find a lot of other sources that include this search term, such as ones about theater production or ballets based on Shakespeare’s play. However, if you’re writing a literary paper on the text of the play itself, you might limit your search results to “fiction” to see only articles that discuss the play within the field of literature. Alternatively, for a theater class discussing the play, you might limit your search results to “drama.”

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New York's viral new trash cans unveiled nearly 2 years after a $1.6 million contract with consultancy giant McKinsey

• New York's viral new trash bins were unveiled following a $1.6 million contract with McKinsey.
• Mayor Eric Adams revealed the new bins as part of his "Trash Revolution," launched Monday.
• New York is embracing containerization — putting your garbage bag in a bin, not just on the street.

New York's much-discussed new trash cans have been shown off nearly two years after the city spent $1.6 million to contract with consulting giant McKinsey in 2022.

On Monday, NYC Mayor Eric Adams revealed the wheeled bin alongside NYC Department of Sanitation Commissioner Jessica Tisch , who symbolically put a black bag from his official residence, Gracie Mansion, into the container.

Video of the launch, which saw Adams wheeling a bin onto the street while Jay-Z and Alicia Keys' "Empire State of Mind" played, quickly went viral, drawing memes and ridicule from citizens of cities that have had similar bins for decades.

Time to Get (EVEN MORE) Stuff Clean! Join us right now in Manhattan as we kick off the next phase in our trash revolution: https://t.co/AEDRQNXmUT — NYC Mayor's Office (@NYCMayorsOffice) July 8, 2024

Under the new rules, which come into force on November 12, 2024, all properties with one to nine residential units will be required by law to use one of the latch-lidded waste collectors, now available to purchase online from $46.

The newly introduced requirement seeks to minimize the number of sidewalk rats . It isn't known exactly how many rats are in NYC, but a 2014 study suggested there were around two million, and a pest control firm in 2023 estimated there were close to three million rats in NYC.

Adams said the program was part of his administration's "Trash Revolution," which aims to clean up the city's streets. Officials estimate New Yorkers produce around 14 billion pounds of trash each year. But with the new rule directed at removing about 70% of this, curbside garbage piles are hoped to become a problem of the past.

Plans for the new bins follow the city's work with consulting giant McKinsey & Company, which was drafted in to help the city assess how to contain its waste. A Sanitation Department official told New York Streetsblog at the time that the project was worth around $4 million, but a spokesperson for the department told Business Insider $1.6 million was paid out to McKinsey for the contract.

According to an October 2022 New York Times article, McKinsey was scheduled to spend 20 weeks working with the Sanitation Department to determine what sort of bins would suit different streetscapes, what they should look like, and which vendors to use. The department told Business Insider that work on the contract concluded in April 2023.

The goal was to design a program capable of combating New York's decadeslong garbage problem, looking at waste collection methods used in urban areas around the world and focusing on containerization — or, in simple terms, putting trash bags in bins rather than on the street.

A New York City Sanitation Department spokesperson told Business Insider, "DSNY's limited work with McKinsey a couple of years ago is not directly related to this week's wheelie bin announcement."

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"McKinsey did not determine or recommend policy — they did math around the fact that the City was interested in waste containerization, a strategy the Adams Administration is now aggressively pursuing," the spokesperson added. The bin design was the result of work conducted by city employees, another DSNY spokesperson told BI.

The solution decided upon is similar to systems already used in cities like Barcelona, where fleets of colored, uniform bins are often found on residential blocks.

The bin project is far from the firm's first consultation in New York. The Office of the New York State Comptroller shows that McKinsey has worked on at least 10 other projects with the state.

McKinsey declined to comment when contacted by Business Insider.

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Preparing and Presenting Effective Research Posters

Associated data.

APPENDIX A.2. Comparison of Research Papers, Presentations, and Posters—Contents.

Posters are a common way to present results of a statistical analysis, program evaluation, or other project at professional conferences. Often, researchers fail to recognize the unique nature of the format, which is a hybrid of a published paper and an oral presentation. This methods note demonstrates how to design research posters to convey study objectives, methods, findings, and implications effectively to varied professional audiences.

A review of existing literature on research communication and poster design is used to identify and demonstrate important considerations for poster content and layout. Guidelines on how to write about statistical methods, results, and statistical significance are illustrated with samples of ineffective writing annotated to point out weaknesses, accompanied by concrete examples and explanations of improved presentation. A comparison of the content and format of papers, speeches, and posters is also provided.

Each component of a research poster about a quantitative analysis should be adapted to the audience and format, with complex statistical results translated into simplified charts, tables, and bulleted text to convey findings as part of a clear, focused story line.

Conclusions

Effective research posters should be designed around two or three key findings with accompanying handouts and narrative description to supply additional technical detail and encourage dialog with poster viewers.

An assortment of posters is a common way to present research results to viewers at a professional conference. Too often, however, researchers treat posters as poor cousins to oral presentations or published papers, failing to recognize the opportunity to convey their findings while interacting with individual viewers. By neglecting to adapt detailed paragraphs and statistical tables into text bullets and charts, they make it harder for their audience to quickly grasp the key points of the poster. By simply posting pages from the paper, they risk having people merely skim their work while standing in the conference hall. By failing to devise narrative descriptions of their poster, they overlook the chance to learn from conversations with their audience.

Even researchers who adapt their paper into a well-designed poster often forget to address the range of substantive and statistical training of their viewers. This step is essential for those presenting to nonresearchers but also pertains when addressing interdisciplinary research audiences. Studies of policymakers ( DiFranza and the Staff of the Advocacy Institute 1996 ; Sorian and Baugh 2002 ) have demonstrated the importance of making it readily apparent how research findings apply to real-world issues rather than imposing on readers to translate statistical findings themselves.

This methods note is intended to help researchers avoid such pitfalls as they create posters for professional conferences. The first section describes objectives of research posters. The second shows how to describe statistical results to viewers with varied levels of statistical training, and the third provides guidelines on the contents and organization of the poster. Later sections address how to prepare a narrative and handouts to accompany a research poster. Because researchers often present the same results as published research papers, spoken conference presentations, and posters, Appendix A compares similarities and differences in the content, format, and audience interaction of these three modes of presenting research results. Although the focus of this note is on presentation of quantitative research results, many of the guidelines about how to prepare and present posters apply equally well to qualitative studies.

WHAT IS A RESEARCH POSTER?

Preparing a poster involves not only creating pages to be mounted in a conference hall, but also writing an associated narrative and handouts, and anticipating the questions you are likely to encounter during the session. Each of these elements should be adapted to the audience, which may include people with different levels of familiarity with your topic and methods ( Nelson et al. 2002 ; Beilenson 2004 ). For example, the annual meeting of the American Public Health Association draws academics who conduct complex statistical analyses along with practitioners, program planners, policymakers, and journalists who typically do not.

Posters are a hybrid form—more detailed than a speech but less than a paper, more interactive than either ( Appendix A ). In a speech, you (the presenter) determine the focus of the presentation, but in a poster session, the viewers drive that focus. Different people will ask about different facets of your research. Some might do policy work or research on a similar topic or with related data or methods. Others will have ideas about how to apply or extend your work, raising new questions or suggesting different contrasts, ways of classifying data, or presenting results. Beilenson (2004) describes the experience of giving a poster as a dialogue between you and your viewers.

By the end of an active poster session, you may have learned as much from your viewers as they have from you, especially if the topic, methods, or audience are new to you. For instance, at David Snowdon's first poster presentation on educational attainment and longevity using data from The Nun Study, another researcher returned several times to talk with Snowdon, eventually suggesting that he extend his research to focus on Alzheimer's disease, which led to an important new direction in his research ( Snowdon 2001 ). In addition, presenting a poster provides excellent practice in explaining quickly and clearly why your project is important and what your findings mean—a useful skill to apply when revising a speech or paper on the same topic.

WRITING FOR A VARIED PROFESSIONAL AUDIENCE

Audiences at professional conferences vary considerably in their substantive and methodological backgrounds. Some will be experts on your topic but not your methods, some will be experts on your methods but not your topic, and most will fall somewhere in between. In addition, advances in research methods imply that even researchers who received cutting-edge methodological training 10 or 20 years ago might not be conversant with the latest approaches. As you design your poster, provide enough background on both the topic and the methods to convey the purpose, findings, and implications of your research to the expected range of readers.

Telling a Simple, Clear Story

Write so your audience can understand why your work is of interest to them, providing them with a clear take-home message that they can grasp in the few minutes they will spend at your poster. Experts in communications and poster design recommend planning your poster around two to three key points that you want your audience to walk away with, then designing the title, charts, and text to emphasize those points ( Briscoe 1996 ; Nelson et al. 2002 ; Beilenson 2004 ). Start by introducing the two or three key questions you have decided will be the focus of your poster, and then provide a brief overview of data and methods before presenting the evidence to answer those questions. Close with a summary of your findings and their implications for research and policy.

A 2001 survey of government policymakers showed that they prefer summaries of research to be written so they can immediately see how the findings relate to issues currently facing their constituencies, without wading through a formal research paper ( Sorian and Baugh 2002 ). Complaints that surfaced about many research reports included that they were “too long, dense, or detailed,” or “too theoretical, technical, or jargony.” On average, respondents said they read only about a quarter of the research material they receive for detail, skim about half of it, and never get to the rest.

To ensure that your poster is one viewers will read, understand, and remember, present your analyses to match the issues and questions of concern to them, rather than making readers translate your statistical results to fit their interests ( DiFranza and the Staff of the Advocacy Institute 1996 ; Nelson et al. 2002 ). Often, their questions will affect how you code your data, specify your model, or design your intervention and evaluation, so plan ahead by familiarizing yourself with your audience's interests and likely applications of your study findings. In an academic journal article, you might report parameter estimates and standard errors for each independent variable in your regression model. In the poster version, emphasize findings for specific program design features, demographic, or geographic groups, using straightforward means of presenting effect size and statistical significance; see “Describing Numeric Patterns and Contrasts” and “Presenting Statistical Test Results” below.

The following sections offer guidelines on how to present statistical findings on posters, accompanied by examples of “poor” and “better” descriptions—samples of ineffective writing annotated to point out weaknesses, accompanied by concrete examples and explanations of improved presentation. These ideas are illustrated with results from a multilevel analysis of disenrollment from the State Children's Health Insurance Program (SCHIP; Phillips et al. 2004 ). I chose that paper to show how to prepare a poster about a sophisticated quantitative analysis of a topic of interest to HSR readers, and because I was a collaborator in that study, which was presented in the three formats compared here—as a paper, a speech, and a poster.

Explaining Statistical Methods

Beilenson (2004) and Briscoe (1996) suggest keeping your description of data and methods brief, providing enough information for viewers to follow the story line and evaluate your approach. Avoid cluttering the poster with too much technical detail or obscuring key findings with excessive jargon. For readers interested in additional methodological information, provide a handout and a citation to the pertinent research paper.

As you write about statistical methods or other technical issues, relate them to the specific concepts you study. Provide synonyms for technical and statistical terminology, remembering that many conferences of interest to policy researchers draw people from a range of disciplines. Even with a quantitatively sophisticated audience, don't assume that people will know the equivalent vocabulary used in other fields. A few years ago, the journal Medical Care published an article whose sole purpose was to compare statistical terminology across various disciplines involved in health services research so that people could understand one another ( Maciejewski et al. 2002 ). After you define the term you plan to use, mention the synonyms from the various fields represented in your audience.

Consider whether acronyms are necessary on your poster. Avoid them if they are not familiar to the field or would be used only once or twice on your poster. If you use acronyms, spell them out at first usage, even those that are common in health services research such as “HEDIS®”(Health Plan Employer Data and Information Set) or “HLM”(hierarchical linear model).

Poor: “We use logistic regression and a discrete-time hazards specification to assess relative hazards of SCHIP disenrollment, with plan level as our key independent variable.” Comment: Terms like “discrete-time hazards specification” may be confusing to readers without training in those methods, which are relatively new on the scene. Also the meaning of “SCHIP” or “plan level” may be unfamiliar to some readers unless defined earlier on the poster.

Better: “Chances of disenrollment from the State Children's Health Insurance Program (SCHIP) vary by amount of time enrolled, so we used hazards models (also known as event history analysis or survival analysis) to correct for those differences when estimating disenrollment patterns for SCHIP plans for different income levels.” Comment: This version clarifies the terms and concepts, naming the statistical method and its synonyms, and providing a sense of why this type of analysis is needed.

To explain a statistical method or assumption, paraphrase technical terms and illustrate how the analytic approach applies to your particular research question and data:

Poor : “The data structure can be formulated as a two-level hierarchical linear model, with families (the level-1 unit of analysis) nested within counties (the level-2 unit of analysis).” Comment: Although this description would be fine for readers used to working with this type of statistical model, those who aren't conversant with those methods may be confused by terminology such as “level-1” and “unit of analysis.”

Better: “The data have a hierarchical (or multilevel) structure, with families clustered within counties.” Comment: By replacing “nested” with the more familiar “clustered,” identifying the specific concepts for the two levels of analysis, and mentioning that “hierarchical” and “multilevel” refer to the same type of analytic structure, this description relates the generic class of statistical model to this particular study.

Presenting Results with Charts

Charts are often the preferred way to convey numeric patterns, quickly revealing the relative sizes of groups, comparative levels of some outcome, or directions of trends ( Briscoe 1996 ; Tufte 2001 ; Nelson et al. 2002 ). As Beilenson puts it, “let your figures do the talking,” reducing the need for long text descriptions or complex tables with lots of tiny numbers. For example, create a pie chart to present sample composition, use a simple bar chart to show how the dependent variable varies across subgroups, or use line charts or clustered bar charts to illustrate the net effects of nonlinear specifications or interactions among independent variables ( Miller 2005 ). Charts that include confidence intervals around point estimates are a quick and effective way to present effect size, direction, and statistical significance. For multivariate analyses, consider presenting only the results for the main variables of interest, listing the other variables in the model in a footnote and including complex statistical tables in a handout.

Provide each chart with a title (in large type) that explains the topic of that chart. A rhetorical question or summary of the main finding can be very effective. Accompany each chart with a few annotations that succinctly describe the patterns in that chart. Although each chart page should be self-explanatory, be judicious: Tufte (2001) cautions against encumbering your charts with too much “nondata ink”—excessive labeling or superfluous features such as arrows and labels on individual data points. Strive for a balance between guiding your readers through the findings and maintaining a clean, uncluttered poster. Use chart types that are familiar to your expected audience. Finally, remember that you can flesh out descriptions of charts and tables in your script rather than including all the details on the poster itself; see “Narrative to Accompany a Poster.”

Describing Numeric Patterns and Contrasts

As you describe patterns or numeric contrasts, whether from simple calculations or complex statistical models, explain both the direction and magnitude of the association. Incorporate the concepts under study and the units of measurement rather than simply reporting coefficients (β's) ( Friedman 1990 ; Miller 2005 ).

Poor: “Number of enrolled children in the family is correlated with disenrollment.” Comment: Neither the direction nor the size of the association is apparent.

Poor [version #2]: “The log-hazard of disenrollment for one-child families was 0.316.” Comment: Most readers find it easier to assess the size and direction from hazards ratios (a form of relative risk) instead of log-hazards (log-relative risks, the β's from a hazards model).

Better: “Families with only one child enrolled in the program were about 1.4 times as likely as larger families to disenroll.” Comment: This version explains the association between number of children and disenrollment without requiring viewers to exponentiate the log-hazard in their heads to assess the size and direction of that association. It also explicitly identifies the group against which one-child families are compared in the model.

Presenting Statistical Test Results

On your poster, use an approach to presenting statistical significance that keeps the focus on your results, not on the arithmetic needed to conduct inferential statistical tests. Replace standard errors or test statistics with confidence intervals, p- values, or symbols, or use formatting such as boldface, italics, or a contrasting color to denote statistically significant findings ( Davis 1997 ; Miller 2005 ). Include the detailed statistical results in handouts for later perusal.

To illustrate these recommendations, Figures 1 and ​ and2 2 demonstrate how to divide results from a complex, multilevel model across several poster pages, using charts and bullets in lieu of the detailed statistical table from the scientific paper ( Table 1 ; Phillips et al. 2004 ). Following experts' advice to focus on one or two key points, these charts emphasize the findings from the final model (Model 5) rather than also discussing each of the fixed- and random-effects specifications from the paper.

Presenting Complex Statistical Results Graphically

Text Summary of Additional Statistical Results

Multilevel Discrete-Time Hazards Models of Disenrollment from SCHIP, New Jersey, January 1998–April 2000

Source : Phillips et al. (2004) .

SCHIP, State Children's Health Insurance Program; LRH, log relative-hazard; SE, standard error.

Figure 1 uses a chart (also from the paper) to present the net effects of a complicated set of interactions between two family-level traits (race and SCHIP plan) and a cross-level interaction between race of the family and county physician racial composition. The title is a rhetorical question that identifies the issue addressed in the chart, and the annotations explain the pattern. The chart version substantially reduces the amount of time viewers need to understand the main take-home point, averting the need to mentally sum and exponentiate several coefficients from the table.

Figure 2 uses bulleted text to summarize other key results from the model, translating log-relative hazards into hazards ratios and interpreting them with minimal reliance on jargon. The results for family race, SCHIP plan, and county physician racial composition are not repeated in Figure 2 , averting the common problem of interpreting main effect coefficients and interaction coefficients without reference to one another.

Alternatively, replace the text summary shown in Figure 2 with Table 2 —a simplified version of Table 1 which presents only the results for Model 5, replaces log-relative hazards with hazards ratios, reports associated confidence intervals in lieu of standard errors, and uses boldface to denote statistical significance. (On a color slide, use a contrasting color in lieu of bold.)

Relative Risks of SCHIP Disenrollment for Other * Family and County Characteristics, New Jersey, January 1998–April 2000

Statistically significant associations are shown in bold.

Based on hierarchical linear model controlling for months enrolled, months-squared, race, SCHIP plan, county physician racial composition, and all variables shown here. Scaled deviance =30,895. Random effects estimate for between-county variance =0.005 (standard error =0.006). SCHIP, State Children's Health Insurance Program; 95% CI, 95% confidence interval.

CONTENTS AND ORGANIZATION OF A POSTER

Research posters are organized like scientific papers, with separate pages devoted to the objectives and background, data and methods, results, and conclusions ( Briscoe 1996 ). Readers view the posters at their own pace and at close range; thus you can include more detail than in slides for a speech (see Appendix A for a detailed comparison of content and format of papers, speeches, and posters). Don't simply post pages from the scientific paper, which are far too text-heavy for a poster. Adapt them, replacing long paragraphs and complex tables with bulleted text, charts, and simple tables ( Briscoe 1996 ; Beilenson 2004 ). Fink (1995) provides useful guidelines for writing text bullets to convey research results. Use presentation software such as PowerPoint to create your pages or adapt them from related slides, facilitating good page layout with generous type size, bullets, and page titles. Such software also makes it easy to create matching handouts (see “Handouts”).

The “W's” (who, what, when, where, why) are an effective way to organize the elements of a poster.

• In the introductory section, describe what you are studying, why it is important, and how your analysis will add to the existing literature in the field.
• In the data and methods section of a statistical analysis, list when, where, who, and how the data were collected, how many cases were involved, and how the data were analyzed. For other types of interventions or program evaluations, list who, when, where, and how many, along with how the project was implemented and assessed.
• In the results section, present what you found.
• In the conclusion, return to what you found and how it can be used to inform programs or policies related to the issue.

Number and Layout of Pages

To determine how many pages you have to work with, find out the dimensions of your assigned space. A 4′ × 8′ bulletin board accommodates the equivalent of about twenty 8.5″ × 11″ pages, but be selective—no poster can capture the full detail of a large series of multivariate models. A trifold presentation board (3′ high by 4′ wide) will hold roughly a dozen pages, organized into three panels ( Appendix B ). Breaking the arrangement into vertical sections allows viewers to read each section standing in one place while following the conventions of reading left-to-right and top-to-bottom ( Briscoe 1996 ).

• At the top of the poster, put an informative title in a large, readable type size. On a 4′ × 8′ bulletin board, there should also be room for an institutional logo.

Suggested Layout for a 4′ × 8′ poster.

• In the left-hand panel, set the stage for the research question, conveying why the topic is of policy interest, summarizing major empirical or theoretical work on related topics, and stating your hypotheses or project aims, and explaining how your work fills in gaps in previous analyses.
• In the middle panel, briefly describe your data source, variables, and methods, then present results in tables or charts accompanied by text annotations. Diagrams, maps, and photographs are very effective for conveying issues difficult to capture succinctly in words ( Miller 2005 ), and to help readers envision the context. A schematic diagram of relationships among variables can be useful for illustrating causal order. Likewise, a diagram can be a succinct way to convey timing of different components of a longitudinal study or the nested structure of a multilevel dataset.
• In the right-hand panel, summarize your findings and relate them back to the research question or project aims, discuss strengths and limitations of your approach, identify research, practice, or policy implications, and suggest directions for future research.

Figure 3 (adapted from Beilenson 2004 ) shows a suggested layout for a 4′ × 8′ bulletin board, designed to be created using software such as Pagemaker that generates a single-sheet presentation; Appendix C shows a complete poster version of the Phillips et al. (2004) multilevel analysis of SCHIP disenrollment. If hardware or budget constraints preclude making a single-sheet poster, a similar configuration can be created using standard 8.5″ × 11″ pages in place of the individual tables, charts, or blocks of text shown in Figure 3 .

Find out well in advance how the posters are to be mounted so you can bring the appropriate supplies. If the room is set up for table-top presentations, tri-fold poster boards are essential because you won't have anything to attach a flat poster board or pages to. If you have been assigned a bulletin board, bring push-pins or a staple gun.

Regardless of whether you will be mounting your poster at the conference or ahead of time, plan how the pages are to be arranged. Experiment with different page arrangements on a table marked with the dimensions of your overall poster. Once you have a final layout, number the backs of the pages or draw a rough sketch to work from as you arrange the pages on the board. If you must pin pages to a bulletin board at the conference venue, allow ample time to make them level and evenly spaced.

Other Design Considerations

A few other issues to keep in mind as you design your poster. Write a short, specific title that fits in large type size on the title banner of your poster. The title will be potential readers' first glimpse of your poster, so make it inviting and easy to read from a distance—at least 40-point type, ideally larger. Beilenson (2004) advises embedding your key finding in the title so viewers don't have to dig through the abstract or concluding page to understand the purpose and conclusions of your work. A caution: If you report a numeric finding in your title, keep in mind that readers may latch onto it as a “factoid” to summarize your conclusions, so select and phrase it carefully ( McDonough 2000 ).

Use at least 14-point type for the body of the poster text. As Briscoe (1996) points out, “many in your audience have reached the bifocal age” and all of them will read your poster while standing, hence long paragraphs in small type will not be appreciated! Make judicious use of color. Use a clear, white, or pastel for the background, with black or another dark color for most text, and a bright, contrasting shade to emphasize key points or to identify statistically significant results ( Davis 1997 ).

NARRATIVE TO ACCOMPANY A POSTER

Prepare a brief oral synopsis of the purpose, findings, and implications of your work to say to interested parties as they pause to read your poster. Keep it short—a few sentences that highlight what you are studying, a couple of key findings, and why they are important. Design your overview as a “sound byte” that captures your main points in a succinct and compelling fashion ( Beilenson 2004 ). After hearing your introduction, listeners will either nod and move along or comment on some aspect of your work that intrigues them. You can then tailor additional discussion to individual listeners, adjusting the focus and amount of detail to suit their interests. Gesture at the relevant pages as you make each point, stating the purpose of each chart or table and explaining its layout before describing the numeric findings; see Miller (2005) for guidelines on how to explain tables and charts to a live audience. Briscoe (1996) points out that these mini-scripts are opportunities for you to fill in details of your story line, allowing you to keep the pages themselves simple and uncluttered.

Prepare short answers to likely questions about various aspects of your work, such as why it is important from a policy or research perspective, or descriptions of data, methods, and specific results. Think of these as little modules from an overall speech—concise descriptions of particular elements of your study that you can choose among in response to questions that arise. Beilenson (2004) also recommends developing a few questions to ask your viewers, inquiring about their reactions to your findings, ideas for additional questions, or names of others working on the topic.

Practice your poster presentation in front of a test audience acquainted with the interests and statistical proficiency of your expected viewers. Ideally, your critic should not be too familiar with your work: A fresh set of eyes and ears is more likely to identify potential points of confusion than someone who is jaded from working closely with the material while writing the paper or drafting the poster ( Beilenson 2004 ). Ask your reviewer to identify elements that are unclear, flag jargon to be paraphrased or defined, and recommend changes to improve clarity ( Miller 2005 ). Have them critique your oral presentation as well as the contents and layout of the poster.

Prepare handouts to distribute to interested viewers. These can be produced from slides created in presentation software, printed several to a page along with a cover page containing the abstract and your contact information. Or package an executive summary or abstract with a few key tables or charts. Handouts provide access to the more detailed literature review, data and methods, full set of results, and citations without requiring viewers to read all of that information from the poster ( Beilenson 2004 ; Miller 2005 ). Although you also can bring copies of the complete paper, it is easier on both you and your viewers if you collect business cards or addresses and mail the paper later.

The quality and effectiveness of research posters at professional conferences is often compromised by authors' failure to take into account the unique nature of such presentations. One common error is posting numerous statistical tables and long paragraphs from a research paper—an approach that overwhelms viewers with too much detail for this type of format and presumes familiarity with advanced statistical techniques. Following recommendations from the literature on research communication and poster design, this paper shows how to focus each poster on a few key points, using charts and text bullets to convey results as part of a clear, straightforward story line, and supplementing with handouts and an oral overview.

Another frequent mistake is treating posters as a one-way means of communication. Unlike published papers, poster sessions are live presentations; unlike speeches, they allow for extended conversation with viewers. This note explains how to create an oral synopsis of the project, short modular descriptions of poster elements, and questions to encourage dialog. By following these guidelines, researchers can substantially improve their conference posters as vehicles to disseminate findings to varied research and policy audiences.

CHECKLIST FOR PREPARING AND PRESENTING AN EFFECTIVE RESEARCH POSTERS

• Design poster to focus on two or three key points.
• Adapt materials to suit expected viewers' knowledge of your topic and methods.
• Design questions to meet their interests and expected applications of your work.
• Paraphrase descriptions of complex statistical methods.
• Spell out acronyms if used.
• Replace large detailed tables with charts or small, simplified tables.
• Accompany tables or charts with bulleted annotations of major findings.
• Describe direction and magnitude of associations.
• Use confidence intervals, p -values, symbols, or formatting to denote statistical significance.

Layout and Format

• Organize the poster into background, data and methods, results, and study implications.
• Divide the material into vertical sections on the poster.
• Use at least 14-point type in the body of your poster, at least 40-point for the title.

Narrative Description

• Rehearse a three to four sentence overview of your research objectives and main findings.
• Summary of key studies and gaps in existing literature
• Data and methods
• Each table, chart, or set of bulleted results
• Research, policy, and practice implications
• Solicit their input on your findings
• Develop additional questions for later analysis
• Identify other researchers in the field
• Prepare handouts to distribute to interested viewers.
• Print slides from presentation software, several to a page.
• Or package an executive summary or abstract with a few key tables or charts.
• Include an abstract and contact information.

Acknowledgments

I would like to thank Ellen Idler, Julie Phillips, Deborah Carr, Diane (Deedee) Davis, and two anonymous reviewers for helpful comments on earlier drafts of this work.

Supplementary Material

The following supplementary material for this article is available online:

APPENDIX A.1. Comparison of Research Papers, Presentations, and Posters—Materials and Audience Interaction.

Suggested Layout for a Tri-Fold Presentation Board.

Example Research Poster of Phillips et al. 2004 Study.

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A new tool makes it easier for database users to perform complicated statistical analyses of tabular data without the need to know what is going on behind the scenes.

GenSQL, a generative AI system for databases, could help users make predictions, detect anomalies, guess missing values, fix errors, or generate synthetic data with just a few keystrokes.

For instance, if the system were used to analyze medical data from a patient who has always had high blood pressure, it could catch a blood pressure reading that is low for that particular patient but would otherwise be in the normal range.

GenSQL automatically integrates a tabular dataset and a generative probabilistic AI model, which can account for uncertainty and adjust their decision-making based on new data.

Moreover, GenSQL can be used to produce and analyze synthetic data that mimic the real data in a database. This could be especially useful in situations where sensitive data cannot be shared, such as patient health records, or when real data are sparse.

This new tool is built on top of SQL, a programming language for database creation and manipulation that was introduced in the late 1970s and is used by millions of developers worldwide.

“Historically, SQL taught the business world what a computer could do. They didn’t have to write custom programs, they just had to ask questions of a database in high-level language. We think that, when we move from just querying data to asking questions of models and data, we are going to need an analogous language that teaches people the coherent questions you can ask a computer that has a probabilistic model of the data,” says Vikash Mansinghka ’05, MEng ’09, PhD ’09, senior author of a paper introducing GenSQL and a principal research scientist and leader of the Probabilistic Computing Project in the MIT Department of Brain and Cognitive Sciences.

When the researchers compared GenSQL to popular, AI-based approaches for data analysis, they found that it was not only faster but also produced more accurate results. Importantly, the probabilistic models used by GenSQL are explainable, so users can read and edit them.

“Looking at the data and trying to find some meaningful patterns by just using some simple statistical rules might miss important interactions. You really want to capture the correlations and the dependencies of the variables, which can be quite complicated, in a model. With GenSQL, we want to enable a large set of users to query their data and their model without having to know all the details,” adds lead author Mathieu Huot, a research scientist in the Department of Brain and Cognitive Sciences and member of the Probabilistic Computing Project.

They are joined on the paper by Matin Ghavami and Alexander Lew, MIT graduate students; Cameron Freer, a research scientist; Ulrich Schaechtle and Zane Shelby of Digital Garage; Martin Rinard, an MIT professor in the Department of Electrical Engineering and Computer Science and member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Feras Saad ’15, MEng ’16, PhD ’22, an assistant professor at Carnegie Mellon University. The research was recently presented at the ACM Conference on Programming Language Design and Implementation.

Combining models and databases

SQL, which stands for structured query language, is a programming language for storing and manipulating information in a database. In SQL, people can ask questions about data using keywords, such as by summing, filtering, or grouping database records.

However, querying a model can provide deeper insights, since models can capture what data imply for an individual. For instance, a female developer who wonders if she is underpaid is likely more interested in what salary data mean for her individually than in trends from database records.

The researchers noticed that SQL didn’t provide an effective way to incorporate probabilistic AI models, but at the same time, approaches that use probabilistic models to make inferences didn’t support complex database queries.

They built GenSQL to fill this gap, enabling someone to query both a dataset and a probabilistic model using a straightforward yet powerful formal programming language.

A GenSQL user uploads their data and probabilistic model, which the system automatically integrates. Then, she can run queries on data that also get input from the probabilistic model running behind the scenes. This not only enables more complex queries but can also provide more accurate answers.

For instance, a query in GenSQL might be something like, “How likely is it that a developer from Seattle knows the programming language Rust?” Just looking at a correlation between columns in a database might miss subtle dependencies. Incorporating a probabilistic model can capture more complex interactions.   

Plus, the probabilistic models GenSQL utilizes are auditable, so people can see which data the model uses for decision-making. In addition, these models provide measures of calibrated uncertainty along with each answer.

For instance, with this calibrated uncertainty, if one queries the model for predicted outcomes of different cancer treatments for a patient from a minority group that is underrepresented in the dataset, GenSQL would tell the user that it is uncertain, and how uncertain it is, rather than overconfidently advocating for the wrong treatment.

Faster and more accurate results

To evaluate GenSQL, the researchers compared their system to popular baseline methods that use neural networks. GenSQL was between 1.7 and 6.8 times faster than these approaches, executing most queries in a few milliseconds while providing more accurate results.

They also applied GenSQL in two case studies: one in which the system identified mislabeled clinical trial data and the other in which it generated accurate synthetic data that captured complex relationships in genomics.

Next, the researchers want to apply GenSQL more broadly to conduct largescale modeling of human populations. With GenSQL, they can generate synthetic data to draw inferences about things like health and salary while controlling what information is used in the analysis.

They also want to make GenSQL easier to use and more powerful by adding new optimizations and automation to the system. In the long run, the researchers want to enable users to make natural language queries in GenSQL. Their goal is to eventually develop a ChatGPT-like AI expert one could talk to about any database, which grounds its answers using GenSQL queries.   

This research is funded, in part, by the Defense Advanced Research Projects Agency (DARPA), Google, and the Siegel Family Foundation.

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Journal of Materials Chemistry A

Research progress of zinc oxide-based heterojunction photocatalysts.

At present, the crisis of declining resources and environmental degradation has become a major global challenge. Photocatalysis is a very efficient technology for the production of renewable energy as well as the degradation of pollutants. Among them, zinc oxide-based photocatalysts have become one of the most promising photocatalysts due to their stability and environmental protection, and they have a wide range of applications in pollutant degradation, water cracking photolysis hydrogen production, CO2 reduction, etc. Although a lot of research has been done on the performance of photocatalysts, there is still a large gap in exploring heterojunctions and their effects on photocatalytic efficiency. In this paper, we aim to fill this gap by in-depth analysis of the influence of different heterojunction structures in zinc oxide-based photocatalysts on various applications. Other effective strategies to improve photocatalytic performance are summarized, which provides important insights for promoting the development of photocatalytic efficiency.

• This article is part of the themed collection: Journal of Materials Chemistry A Recent Review Articles

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J. Liu, H. Wang, H. Wu, Y. Yang, C. Wang, Q. Wang, B. Jia and J. Zheng, J. Mater. Chem. A , 2024, Accepted Manuscript , DOI: 10.1039/D4TA03901G

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