what is population sample in research

Unraveling Research Population and Sample: Understanding their role in statistical inference

Research population and sample serve as the cornerstones of any scientific inquiry. They hold the power to unlock the mysteries hidden within data. Understanding the dynamics between the research population and sample is crucial for researchers. It ensures the validity, reliability, and generalizability of their findings. In this article, we uncover the profound role of the research population and sample, unveiling their differences and importance that reshapes our understanding of complex phenomena. Ultimately, this empowers researchers to make informed conclusions and drive meaningful advancements in our respective fields.

Table of Contents

What Is Population?

The research population, also known as the target population, refers to the entire group or set of individuals, objects, or events that possess specific characteristics and are of interest to the researcher. It represents the larger population from which a sample is drawn. The research population is defined based on the research objectives and the specific parameters or attributes under investigation. For example, in a study on the effects of a new drug, the research population would encompass all individuals who could potentially benefit from or be affected by the medication.

When Is Data Collection From a Population Preferred?

In certain scenarios where a comprehensive understanding of the entire group is required, it becomes necessary to collect data from a population. Here are a few situations when one prefers to collect data from a population:

1. Small or Accessible Population

When the research population is small or easily accessible, it may be feasible to collect data from the entire population. This is often the case in studies conducted within specific organizations, small communities, or well-defined groups where the population size is manageable.

2. Census or Complete Enumeration

In some cases, such as government surveys or official statistics, a census or complete enumeration of the population is necessary. This approach aims to gather data from every individual or entity within the population. This is typically done to ensure accurate representation and eliminate sampling errors.

3. Unique or Critical Characteristics

If the research focuses on a specific characteristic or trait that is rare and critical to the study, collecting data from the entire population may be necessary. This could be the case in studies related to rare diseases, endangered species, or specific genetic markers.

4. Legal or Regulatory Requirements

Certain legal or regulatory frameworks may require data collection from the entire population. For instance, government agencies might need comprehensive data on income levels, demographic characteristics, or healthcare utilization for policy-making or resource allocation purposes.

5. Precision or Accuracy Requirements

In situations where a high level of precision or accuracy is necessary, researchers may opt for population-level data collection. By doing so, they mitigate the potential for sampling error and obtain more reliable estimates of population parameters.

What Is a Sample?

A sample is a subset of the research population that is carefully selected to represent its characteristics. Researchers study this smaller, manageable group to draw inferences that they can generalize to the larger population. The selection of the sample must be conducted in a manner that ensures it accurately reflects the diversity and pertinent attributes of the research population. By studying a sample, researchers can gather data more efficiently and cost-effectively compared to studying the entire population. The findings from the sample are then extrapolated to make conclusions about the larger research population.

What Is Sampling and Why Is It Important?

Sampling refers to the process of selecting a sample from a larger group or population of interest in order to gather data and make inferences. The goal of sampling is to obtain a sample that is representative of the population, meaning that the sample accurately reflects the key attributes, variations, and proportions present in the population. By studying the sample, researchers can draw conclusions or make predictions about the larger population with a certain level of confidence.

Collecting data from a sample, rather than the entire population, offers several advantages and is often necessary due to practical constraints. Here are some reasons to collect data from a sample:

1. Cost and Resource Efficiency

Collecting data from an entire population can be expensive and time-consuming. Sampling allows researchers to gather information from a smaller subset of the population, reducing costs and resource requirements. It is often more practical and feasible to collect data from a sample, especially when the population size is large or geographically dispersed.

2. Time Constraints

Conducting research with a sample allows for quicker data collection and analysis compared to studying the entire population. It saves time by focusing efforts on a smaller group, enabling researchers to obtain results more efficiently. This is particularly beneficial in time-sensitive research projects or situations that necessitate prompt decision-making.

3. Manageable Data Collection

Working with a sample makes data collection more manageable . Researchers can concentrate their efforts on a smaller group, allowing for more detailed and thorough data collection methods. Furthermore, it is more convenient and reliable to store and conduct statistical analyses on smaller datasets. This also facilitates in-depth insights and a more comprehensive understanding of the research topic.

4. Statistical Inference

Collecting data from a well-selected and representative sample enables valid statistical inference. By using appropriate statistical techniques, researchers can generalize the findings from the sample to the larger population. This allows for meaningful inferences, predictions, and estimation of population parameters, thus providing insights beyond the specific individuals or elements in the sample.

5. Ethical Considerations

In certain cases, collecting data from an entire population may pose ethical challenges, such as invasion of privacy or burdening participants. Sampling helps protect the privacy and well-being of individuals by reducing the burden of data collection. It allows researchers to obtain valuable information while ensuring ethical standards are maintained .

Key Steps Involved in the Sampling Process

Sampling is a valuable tool in research; however, it is important to carefully consider the sampling method, sample size, and potential biases to ensure that the findings accurately represent the larger population and are valid for making conclusions and generalizations. While the specific steps may vary depending on the research context, here is a general outline of the sampling process:

1. Define the Population

Clearly define the target population for your research study. The population should encompass the group of individuals, elements, or units that you want to draw conclusions about.

2. Define the Sampling Frame

Create a sampling frame, which is a list or representation of the individuals or elements in the target population. The sampling frame should be comprehensive and accurately reflect the population you want to study.

3. Determine the Sampling Method

Select an appropriate sampling method based on your research objectives, available resources, and the characteristics of the population. You can perform sampling by either utilizing probability-based or non-probability-based techniques. Common sampling methods include random sampling, stratified sampling, cluster sampling, and convenience sampling.

4. Determine Sample Size

Determine the desired sample size based on statistical considerations, such as the level of precision required, desired confidence level, and expected variability within the population. Larger sample sizes generally reduce sampling error but may be constrained by practical limitations.

5. Collect Data

Once the sample is selected using the appropriate technique, collect the necessary data according to the research design and data collection methods . Ensure that you use standardized and consistent data collection process that is also appropriate for your research objectives.

6. Analyze the Data

Perform the necessary statistical analyses on the collected data to derive meaningful insights. Use appropriate statistical techniques to make inferences, estimate population parameters, test hypotheses, or identify patterns and relationships within the data.

Population vs Sample — Differences and examples

While the population provides a comprehensive overview of the entire group under study, the sample, on the other hand, allows researchers to draw inferences and make generalizations about the population. Researchers should employ careful sampling techniques to ensure that the sample is representative and accurately reflects the characteristics and variability of the population.

Research Study: Investigating the prevalence of stress among high school students in a specific city and its impact on academic performance.

Population: All high school students in a particular city

Sampling Frame: The sampling frame would involve obtaining a comprehensive list of all high schools in the specific city. A random selection of schools would be made from this list to ensure representation from different areas and demographics of the city.

Sample: Randomly selected 500 high school students from different schools in the city

The sample represents a subset of the entire population of high school students in the city.

Research Study: Assessing the effectiveness of a new medication in managing symptoms and improving quality of life in patients with the specific medical condition.

Population: Patients diagnosed with a specific medical condition

Sampling Frame: The sampling frame for this study would involve accessing medical records or databases that include information on patients diagnosed with the specific medical condition. Researchers would select a convenient sample of patients who meet the inclusion criteria from the sampling frame.

Sample: Convenient sample of 100 patients from a local clinic who meet the inclusion criteria for the study

The sample consists of patients from the larger population of individuals diagnosed with the medical condition.

Research Study: Investigating community perceptions of safety and satisfaction with local amenities in the neighborhood.

Population: Residents of a specific neighborhood

Sampling Frame: The sampling frame for this study would involve obtaining a list of residential addresses within the specific neighborhood. Various sources such as census data, voter registration records, or community databases offer the means to obtain this information. From the sampling frame, researchers would randomly select a cluster sample of households to ensure representation from different areas within the neighborhood.

Sample: Cluster sample of 50 households randomly selected from different blocks within the neighborhood

The sample represents a subset of the entire population of residents living in the neighborhood.

To summarize, sampling allows for cost-effective data collection, easier statistical analysis, and increased practicality compared to studying the entire population. However, despite these advantages, sampling is subject to various challenges. These challenges include sampling bias, non-response bias, and the potential for sampling errors.

To minimize bias and enhance the validity of research findings , researchers should employ appropriate sampling techniques, clearly define the population, establish a comprehensive sampling frame, and monitor the sampling process for potential biases. Validating findings by comparing them to known population characteristics can also help evaluate the generalizability of the results. Properly understanding and implementing sampling techniques ensure that research findings are accurate, reliable, and representative of the larger population. By carefully considering the choice of population and sample, researchers can draw meaningful conclusions and, consequently, make valuable contributions to their respective fields of study.

Now, it’s your turn! Take a moment to think about a research question that interests you. Consider the population that would be relevant to your inquiry. Who would you include in your sample? How would you go about selecting them? Reflecting on these aspects will help you appreciate the intricacies involved in designing a research study. Let us know about it in the comment section below or reach out to us using  #AskEnago  and tag  @EnagoAcademy  on  Twitter ,  Facebook , and  Quora .

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Guide to population vs. sample in research

Last updated

29 May 2023

Reviewed by

Miroslav Damyanov

Population data consists of information collected from every individual in a particular population. Meanwhile, sample data consists of information taken from a subset—or sample —of the population.

In this guide, we’ll discuss the differences between population and sample data, the advantages and disadvantages of each, how to collect data from a sample and a population, and common sampling techniques . By the end, you'll have a better understanding of the differences between population and sample data and when to use them.

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• What is "population" in research?

Population data is the total number of measurements taken from every individual within a group. For example, if you were measuring the heights of all humans on Earth, you’d include all 7 billion people in your population data set. 

When analyzing population data, researchers use statistics such as the population mean, median, and standard deviation. 

Types of populations

Finite population.

A finite population is a population in which all the members are known and can be counted. Examples of this type of population include all the employees of a company, all the students in a school, or the entire population of a city. When working with a finite population, you can calculate the exact population mean, median, and standard deviation.

Infinite population

An infinite population is a population that is too large to be measured or counted. This could be the entire human population on Earth or the number of stars in the sky. Because it’s impossible to measure or count these populations, it isn’t possible to calculate their exact mean, median, and standard deviation.

Closed population

A closed population is one in which you allow no new members to join. An example of a closed population would be a country's citizens over the age of 18 who have been living there for more than 10 years. As no new members can join, the population remains constant and can easily be measured and analyzed.

Open population

An open population is one in which new members can join. For example, all people living in a certain city are considered an open population because new members can move into the city and become part of the population. This type of population is constantly changing, so it isn’t possible to measure and analyze its exact characteristics.

Advantages of population data

Representative.

It offers a complete representation of all elements in the population, which can increase the generalizability of findings.

High quality

Population data is usually very accurate and detailed because standardized data collection methods and quality control measures are in place to provide data from every element in the population.

Large sample size

The sample size is large, which can increase the statistical power of a study and help detect small but meaningful differences. 

Can address rare events

You can use population data to study rare events or diseases that wouldn’t be feasible to study through other methods.

Allows for subgroup analysis

You can use population data to examine subgroups of the population, which can help identify disparities and inform interventions. 

Disadvantages of population data

Time and cost constraints.

Collecting data from a large population is expensive and time-consuming, especially when it comes to data cleaning and preparation before using it for analysis.

Limited access

Depending on the source of population data, it can be difficult to get access to the population or convince people to participate, especially when there are privacy concerns or restrictions on the use of data.

Limited variables

Population data may have limited variables or lack information on important factors, which may not allow one to answer a particular research question if the data wasn’t originally collected for that purpose.

Difficult to analyze

Population data can be large, complex, and contain a wide variety of data or even missing data which demands advanced analytical skills and high computational requirements. 

Outdated information

Population data may become outdated, especially if it was collected some time ago, which can limit its relevance to current research questions. 

• What is a sample in research?

Sampling is the process of selecting individuals from a larger population and is used to generate representative information about the population of interest. There are two forms of sampling: non-probability. 

Probability sampling is from a randomly selected small subset and provides statistical inferences about the whole population without bias. Non-probability sampling collects data from a selected subset chosen for its convenience or, sometimes, to control and manipulate the data collected.

Types of probability sampling

Random sampling.

This type of sampling is completely by chance. Each member of the population has an equal chance of being selected for the sample, and the results of a random sample will be statistically representative of the whole population. 

For example, if you wanted to know how people felt about a new product, you could use a random number generator to select members from a population for the study.

Stratified sampling

Stratified sampling is when the population is split into different subgroups, or strata, based on one or more characteristics. The researcher then randomly selects members from each stratum to represent the population. This allows the researcher to accurately compare data between different groups because it ensures that all subgroups are represented in the sample. 

For example, if you wanted to measure the opinion of people in different age groups, you could divide your population into groups based on age and then take random samples from each stratum.

Cluster sampling

This type of sampling divides the population into clusters or groups and then further takes a sample from each cluster. This method is often used when it isn’t possible to access the entire population. 

For example, if you wanted to measure public opinion on an issue in a large city, it wouldn’t be feasible to survey every single person. Instead, you could divide the city into neighborhoods and take random samples from each one.

Systematic sample

Systematic sampling involves selecting items from a population based on a set pattern or system. This type of sampling is useful when it’s impossible or impractical to create a list of all items in a population. It’s similar to random sampling in that it helps eliminate any bias from the selection process, but it’s more efficient because it requires fewer samples to be taken. 

If a researcher can only select 10 members from a population of 200 people, they could use systematic sampling by selecting every 20th person in the list to eliminate bias.

Types of non-probability sampling

Convenience sampling.

This form of sampling involves selecting participants based on availability and willingness to take part. This can lead to volunteer bias, meaning that individuals who are more motivated or have more time may be more likely to participate.

Quota sampling

A method of selecting participants from a larger population to match certain criteria is referred to as quota sampling. For example, market researchers might use quota sampling to select a certain number of individuals within specific age groups.

Judgemental sampling

This technique is also referred to as purposive sampling or authoritative sampling. You can use it to target specific individuals who possess a certain set of qualities like age, ethnicity, or religious beliefs. It can help researchers access important information from people with specific knowledge or experience. 

However, this kind of sampling can also lead to selection bias, which is the distortion of results due to the non-random selection of participants.

Snowball sampling

Snowball sampling is often used to reach individuals who may be difficult to access through traditional means. This type of sampling involves asking participants to refer others who fit the same criteria. It’s often used in social sciences research to identify people within a certain community or social group. For example, researchers may conduct a survey offering a reward to participants who refer their close friends or family and get them to participate.  

While this technique can be useful in reaching underserved or underrepresented populations, it also carries the risk of selection bias.

Advantages of sample data

Cost-effective.

Collecting data from a sample is typically less expensive and time-consuming than collecting data from an entire population.  

Higher quality

Collecting data from a smaller subset of a population can often result in higher-quality data when more resources are dedicated to ensuring the accuracy and completeness of the data. 

Feasibility

In some cases, it may be impossible or impractical to collect data from an entire population, making sample data a more feasible option. 

Sample data is usually smaller and more manageable than population data, which makes it easier to analyze. 

Reduced sampling bias

With appropriate sampling methods, sample data can be representative of the large population and provide valuable insights for research. 

Disadvantages of using sample data

Generalizability.

The quality of the data depends on the quality of the sample selection process. If the sample isn’t representative of the population, it leads to skewed results.

Sampling bias

A sample may not provide a complete picture of an entire population when certain groups are overrepresented or underrepresented in the sample.  

Sampling error

Because sample data is drawn from a subset of a larger population, there is always a risk of sampling error . It occurs when the sample doesn’t accurately represent the larger population, which can lead to inaccurate results.

Statistical power

A small sample size can limit the statistical power of the data analysis, making it more difficult to detect meaningful differences or relationships between studied variables. 

Limited score

Sample data may be limited in scope and may not capture the full range of variables present in an entire population. This can limit the depth and breadth of the findings.

• Differences between population and sample

When discussing research and data analysis, it’s important to understand the differences between population and sample data. Here are some key points to consider when distinguishing between the two: 

Population vs. sample

A population is a set of all individuals or objects that share a common characteristic, while a sample is a subset of that population used to draw conclusions about the entire population. 

For example, if you wanted to research the opinions of all people living in the United States, the population would be all citizens in the US, while the sample would be a smaller subset of people surveyed to represent the opinion of the entire population.

Sample vs. population mean

The sample mean is an average of a sample's values, while the population mean is an average of all values in a population. For example, if you’re researching the average income of households in America, the sample mean would be an average of incomes from a smaller group of households selected from the population of all households in the US.

Sample vs. population standard deviation

Standard deviation measures the variation of a set of values from their mean. The sample standard deviation is based on the variation within a sample, while the population standard deviation is based on the variation within a population. 

For example, if you were researching the variation in test scores for students at a particular school, the sample standard deviation would be based on the scores of a smaller subset of students from the school, while the population standard deviation would be based on all scores from every student at the school.

• How to collect and use data from a sample

1. Choose the right sampling technique

The most common sampling techniques include random, stratified, convenience, and cluster sampling . Selecting the right technique for your research will depend on your specific needs, resources, goals, and objectives.

2. Decide the sample size

Determining the sample size will vary depending on the goal of your research. Generally speaking, the larger the sample size, the more reliable your results will be. However, there are tradeoffs, such as the cost and resources required to collect data from larger samples.

3. Design an instrument for collecting data

Once you've chosen your sampling technique and decided on the sample size, you'll need to design an instrument for collecting data. This could include surveys , interviews, or experiments. Make sure that the instrument is valid and reliable so that it provides accurate results.

4. Determine a sample frame 

Decide who you’ll include in the sample by selecting the population or subpopulation you want to study. Consider factors like location, age, gender, behavior, and so on when choosing your sample frame.

5. Execute the sample selection process

In this step, you'll select individuals to form your sample. To ensure accuracy, it’s best to use random sampling techniques to guarantee a representative sample.

6. Collect data from a sample

Once you’ve selected the sample, you can begin collecting data. Depending on the method you chose (e.g., survey, interview, experiment), you may need to do some additional steps before you can begin collecting data:

For example, if you’re collecting data through a survey, you may need to obtain permission to conduct the survey from relevant authorities, such as a workplace or community group.

If you plan to conduct interviews as your data collection method, ensure your questions are well-formed and that your interviewees are comfortable answering them. Before the interview, you may also want to send a pre-interview questionnaire to participants to collect basic information to make the interview process more efficient.

Most experiments require a significant amount of planning and preparation to ensure that data is collected in a controlled and systematic manner. Additionally, you may need to consider the ethical implications of conducting the experiment, such as obtaining informed consent from participants and ensuring their safety throughout the experiment.

7. Analyze the data

After you've collected data from the sample, analyze it to find meaningful patterns and trends that you can use to draw conclusions about the population. Remember, since you're working with a sample, your conclusions may not apply to the entire population. 

By following these steps, you can easily collect data from a sample to gain insights about a population without having to analyze all of the data from the population itself. When used correctly, sample data can provide valuable insights that can help shape your research conclusions.

• How to collect and use data from a population

1. Define the population

Before collecting data from a population, it’s important to first clearly define what population you’re looking to collect data from. This definition should be as specific as possible and include any relevant behavioral characteristics (e.g., shopping frequency, product use, or commute options) or demographic characteristics (e.g., age, gender, and geography).

2. Create a comprehensive list

After identifying the population in terms of traits, past experiences, outlooks, or other components, create a comprehensive list of the population you’ll be studying. Depending on the purpose of the study, this could include both people and organizations.

3. Contact population and collect data

Once you’ve defined the population and chosen your sampling method, it’s time to collect data. You can obtain this data by conducting experiments, surveys, or interviews. Make sure to collect feedback from every person or entity on the population list to generate an exhaustive population sample.

4. Analyze the data

After collecting the data, it’s important to analyze it to draw meaningful conclusions about the population. This analysis should include calculating the sample mean and sample standard deviation for the data set, as well as comparing these values to the population mean and population standard deviation.

5. Draw conclusions

Once you’ve analyzed the data, use the results to draw conclusions about the population. Make sure to be as accurate and objective as possible when making claims about the population.

• Choosing high-quality samples

High-quality samples are essential when it comes to research. A high-quality sample will produce accurate and reliable study results. A poor-quality sample can result in incorrect or inexact data. These results can be costly and time-consuming to fix. 

A good-quality sample is representative of the population. That means the sample has similar characteristics as the population in terms of age, gender, race, and other factors. The sample should also be randomly selected so as not to bias the results. In addition, the sample should be of a large enough size to be statistically significant .

How to select a high-quality sample

Choose a probability sampling method.

Random selection is the most important part of choosing a high-quality sample. You want to ensure that the sample truly represents the population and that no bias has been introduced. You can do this through methods such as random sampling, stratified sampling, cluster sampling, and systematic sampling. 

Monitor selection process

You should monitor the selection process to ensure that no bias has been introduced during the selection process. You should also make sure that the sample size is large enough to be statistically significant. 

Test for accuracy

You should test the accuracy of your sample by comparing it to the population data. Compare the sample mean vs. population mean, sample vs. population standard deviation, and other factors. If there are any discrepancies between the two, then the sample may not be representative of the population and should be re-evaluated.

By following these steps, you can ensure that your sample is quality and that it correctly reflects the population and produces precise and accurate results.

Using sample and population data can be beneficial in many ways. For example, using sample data allows researchers to make more efficient use of resources while still being able to conclude the population. Additionally, sample data is useful in making statistical inferences about a population, such as the mean or standard deviation. 

On the other hand, population data provides an accurate representation of the whole population, which can be beneficial when researchers need detailed information. 

To ensure accurate and representative data, researchers must understand the differences between populations and weigh the advantages and risks of each sampling technique. By understanding the difference between population and sample data, researchers can gain valuable insights about their target group and use these insights to make informed decisions.

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Population vs Sample – Definitions, Types & Examples

Published by Alvin Nicolas at September 20th, 2021 , Revised On July 19, 2023

Wondering who wins in the Population vs. Sample battle? Don’t know which one to choose for your survey?

If you are hunting similar questions, congratulations, you have come to the right place.

The Sample and Population sections tend to be a stumbling block for most students, if not all. And if you are one of those people, now is the perfect time to seize an opportunity. This guide contains all the information in the world to sweep through the methodology section of your dissertation proficiently.

Sounds interesting? Let’s get started then!

What is Population in Research?

Population in the research market comprises all the members of a defined group that you generalize to find the results of your study. This means the exact population will always depend on the scope of your respected study. Population in research is not limited to assessing humans; it can be any data parameter, including events, objects, histories, and more possessing a common trait. The measurable quality of the population is called a parameter .

For instance…

If you are to evaluate findings for Health Concerns of Women , you might have to consider all the women in the world that are dead, alive, and will live in the future.

Types of Population

Though there are different types and sub-categories of population, below are the four most common yet important ones to consider.

Countable Population

As the term itself explains, this type of population is one that can be numbered and calculated. It is also known as  finite population . An example of a finite or countable population would be all the students in a college or potential buyers of a brand. A countable population in statistical analysis is thought to be of more benefit than other types.

Uncountable Population

The uncountable population, primarily known as an infinite population, is where the counting units are beyond one’s consideration and capabilities. For instance, the number of rice grains in the field. Or the total number of protons and electrons on a blank page. The fact that this type of population cannot be calculated often leaves room for error and uncertainty.

Hypothetical Population 

This is the population whose unit is not available in a tangible form. Although the population in research analysis includes all sets of possible observations, events, and objects, there still are situations that can only be hypothetical. The perfect example to explain this would be the population of the world. You can give an estimated and hypothetical value gathered by different governments, but can you count all humans existing on the planet? Certainly, no! Another example would be the outcome of rolling dice.

Existent Population

The existent population is the opposite of a hypothetical population, i.e., everything is countable in a concrete form. All the notebooks and pens of students of a particular class could be an example of an existent population.

Is all clear?

Let us move on to the next important term of this guide.

What is Sample in Research?

In quantitative research methodology , the sample is a set of collected data from a defined procedure. It is basically a much smaller part of the whole, i.e., population. The sample depicts all the members of the population that are under observation when conducting research surveys . It can be further assessed to find out about the behavior of the entire population data. The measurable quality of the sample is called a statistic .

Say you send a research questionnaire to all the 200 contacts on your phone, and 42 of them end up filling up the forms. Your sample here is the 42 contacts that participated in the study. The rest of the people who did not participate but were sent invitations become part of your  sampling frame . The sampling frame is the group of people who could possibly be in your research or can be a good fit, which here are the 158 people on your phone.

Can you think of more examples? 

Before we start with the sampling types, here are a few other terminologies related to sampling for a better understanding.

Sample Size : the total number of people selected for the survey/study

Sample Technique : The technique you use in order to get your desired sample size.

Pro Tip: Use a sample for your research when you have a larger population, and you want to generalize your findings for the entire population from this sample.

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• Find out by hiring an expert from ResearchProspect today!
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Types of Sampling Methods

There are two major types of sampling; Probability Sampling and Non-probability Sampling.

Probability Sampling

In this type of sampling, the researcher tends to set a selection of a few criteria and selects members of a population randomly. This means all the members have an equal chance to be a part of the study.

For example, you are to examine a bag containing rice or some other food item. Now any small portion or part you take for observation will be a true representative of the whole food bag.

It is further divided into the following five types:

• Simple Random Sampling

In this type of probability sampling, the members of the study are chosen by chance or randomly. Wondering if this affects the overall quality of your research? Well, it does not. The fact that every member has an equal chance of being selected, this random selection will do just as fine and speak well for the whole group. The only thing you need to make sure of is that the population is  homogenous , like the bag of rice.

• Systematic Sampling

In systematic sampling, the researcher will select a member after a fixed interval of time. The member selected for the study after this fixed interval is known as the  Kth element.  

For example, if the researcher decides to select a member occurring after every 30 members, the Kth element here would be the 30th element.

• Stratified Random Sampling

If you know the meaning of strata, you might have guessed by now what stratified random sampling is. So, in this type of sampling, the population is first divided into sub-categories. There is no hard and fast rule for it; it is all done randomly.

So, when do we need this kind of sampling?

Stratified random sampling is adopted when the population is not homogenous. It is first divided into groups and categories based on similarities, and later members from each group are randomly selected. The idea is to address the problem of less homogeneity of the population to get a truly representative sample.

• Cluster Sampling

This is where researchers divide the population into clusters that tend to represent the whole population. They are usually divided based on demographic parameters , such as location, age, and sex. It can be a little difficult than the ones earlier mentioned, but cluster sampling is one of the most effective ways to derive interface from the feedback.

For example, suppose the United States government wishes to evaluate the number of people taking the first dose of the COVID-19 vaccine. In that case, they can divide it into groups based on various country estates. Not only will the results be accurate using this sampling method, but it will also be easier for future diagnoses.

• Multi-stage Sampling

Multi-stage sampling is similar to cluster sampling, but let’s say, a complex form of it. In this type of cluster sampling, all the clusters are further divided into sub-clusters. It involves multiple stages, thus the name. Initially, the naturally occurring categories in a population are chosen as clusters, then each cluster is categorized into smaller clusters, and lastly, members are selected from each smaller cluster.

How many stages are enough?

Well, that depends on the nature of your study/research. For some, two to three would be more than enough, while others can take up to 10 rounds or more.

Non-Probability Sampling

Non-probability sampling is the other sampling type where you cannot calculate the probability or chances of any members selected for research. In other words, it is everything the probability sampling is NOT. We just figured out that probability sampling includes selection by chance; this one depends on the subjective judgment of the researcher.

For example, one member might have a 20 percent chance of getting selected in non-probability sampling, while another could have a 60 percent chance.

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Which type of sampling do you think is better?

The debate on this might prevail forever because there is no correct answer for this. Both have their advantages and disadvantages. While non-probability sampling cannot be reliable, it does save your time and costs. Similarly, if probability sampling yields accurate results, it also is not easy to use and sometimes impossible to be conducted, especially when you have a small population at hand.

Types of Non-Probability Sampling

The Four types of non-probability sampling are:

• Convenience Sampling

Convenience sampling relies on the ease of access to specific subjects such as students in the college café or pedestrians on the road. If the researcher can conveniently get the sample for their study, it will fall under this type of sampling. This type of sampling is usually effective when researchers lack time, resources, and money. They have almost zero authority to choose the sample elements and are purely done on immediacy. You send your questionnaire to random contacts on your phone would be convenience sampling as you did not walk extra miles to get the job done.

• Purposive Sampling

Purposive sampling is also known as judgmental sampling because researchers here would effectively consider the study’s purpose and some understanding of what to expect from the target audience. In other words, the target audience is defined here. For instance, if a study is conducted exclusively for Coronavirus patients, all others not affected by the virus will automatically be rejected or excluded from the study.

• Quota Sampling

For quota sampling, you need to have a pre-set standard of sample selection. What happens in quota sampling is that the sample is formed on the basis of specific attributes so that the qualities of this sample can be found in the total population. Slightly complex but worth the hassle.

• Snowball Sampling

Lastly, this type of non-probability sampling is applied when the subjects are rare and difficult to get. For example, if you are to trace and research drug dealers, it would be almost impossible to get them interviewed for the study. This is where snowball sampling comes into play. Similarly, writing a paper on the mental health of rape victims would also be a hard row to hoe. In such a situation, you will only tract a few sources/members and base the rest of your research on it.

To put it briefly, your sample is the group of people participating in the study, while the population is the total number of people to whom the results will apply. As an analogy, if the sample is the garden in your house, the population will be the forests out there.

Now that you have all the details on these two,  can you spot three differences between population and sample ?

Well, we are sure you can give more than just three.

Here are a few differences in case you need a quick revision.

Differences between Population and Sample

This brings us to the end of this guide. We hope you are now clear on these topics and have made up your mind to use a sample for your research or population. The final choice is yours; however, make sure to keep all the above-mentioned facts and particulars in mind and see what works best for you.

Meanwhile, if you have questions and queries or wish to add to this guide, please drop a comment in the comments section below.

FAQs About Population vs. Sample

How can you identify a sample and population.

Sample is the specific group you collect data from, and the population is the entire group you deduce conclusions about. The population is the bigger sample size.

What is a population parameter?

Parameter is some characteristic of the population that cannot be studied directly. It is usually estimated by numbers and figures calculated from the sample data.

Is it better to use a sample instead of a population?

Yes, if you looking for a cost-effective and easier way, a sample is the better option.

What is an example of statistics?

If one office is the sample of the population of all offices in a building, then the average of salaries earned by all employees in the sample office annually would be an example of a statistic .

Does a sample represent the entire population?

Not always. Only a representative sample reflects the entire population of your study. It is an unbiased reflection of what the population is actually like. For instance, you can evaluate the effectiveness by dividing your population on the basis of gender, education, profession, and so on. It depends on how much information is available about your population and the scope of your study. Not to mention how detailed you want your study to be.

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• v.19(1); Jan-Jun 2010

Statistics without tears: Populations and samples

Amitav banerjee.

Department of Community Medicine, D Y Patil Medical College, Pune, India

Suprakash Chaudhury

1 Department of Psychiatry, RINPAS, Kanke, Ranchi, India

Research studies are usually carried out on sample of subjects rather than whole populations. The most challenging aspect of fieldwork is drawing a random sample from the target population to which the results of the study would be generalized. In actual practice, the task is so difficult that some sampling bias occurs in almost all studies to a lesser or greater degree. In order to assess the degree of this bias, the informed reader of medical literature should have some understanding of the population from which the sample was drawn. The ultimate decision on whether the results of a particular study can be generalized to a larger population depends on this understanding. The subsequent deliberations dwell on sampling strategies for different types of research and also a brief description of different sampling methods.

Research workers in the early 19th century endeavored to survey entire populations. This feat was tedious, and the research work suffered accordingly. Current researchers work only with a small portion of the whole population (a sample) from which they draw inferences about the population from which the sample was drawn.

This inferential leap or generalization from samples to population, a feature of inductive or empirical research, can be full of pitfalls. In clinical medicine, it is not sufficient merely to describe a patient without assessing the underlying condition by a detailed history and clinical examination. The signs and symptoms are then interpreted against the total background of the patient's history and clinical examination including mental state examination. Similarly, in inferential statistics, it is not enough to just describe the results in the sample. One has to critically appraise the real worth or representativeness of that particular sample. The following discussion endeavors to explain the inputs required for making a correct inference from a sample to the target population.

TARGET POPULATION

Any inferences from a sample refer only to the defined population from which the sample has been properly selected. We may call this the target population. For example, if in a sample of lawyers from Delhi High Court it is found that 5% are having alcohol dependence syndrome, can we say that 5% of all lawyers all over the world are alcoholics? Obviously not, as the lawyers of Delhi High Court may be an institution by themselves and may not represent the global lawyers′ community. The findings of this study, therefore, apply only to Delhi High Court lawyers from which a representative sample was taken. Of course, this finding may nevertheless be interesting, but only as a pointer to further research. The data on lawyers in a particular city tell us nothing about lawyers in other cities or countries.

POPULATIONS IN INFERENTIAL STATISTICS

In statistics, a population is an entire group about which some information is required to be ascertained. A statistical population need not consist only of people. We can have population of heights, weights, BMIs, hemoglobin levels, events, outcomes, so long as the population is well defined with explicit inclusion and exclusion criteria. In selecting a population for study, the research question or purpose of the study will suggest a suitable definition of the population to be studied, in terms of location and restriction to a particular age group, sex or occupation. The population must be fully defined so that those to be included and excluded are clearly spelt out (inclusion and exclusion criteria). For example, if we say that our study populations are all lawyers in Delhi, we should state whether those lawyers are included who have retired, are working part-time, or non-practicing, or those who have left the city but still registered at Delhi.

Use of the word population in epidemiological research does not correspond always with its demographic meaning of an entire group of people living within certain geographic or political boundaries. A population for a research study may comprise groups of people defined in many different ways, for example, coal mine workers in Dhanbad, children exposed to German measles during intrauterine life, or pilgrims traveling to Kumbh Mela at Allahabad.

GENERALIZATION (INFERENCES) FROM A POPULATION

When generalizing from observations made on a sample to a larger population, certain issues will dictate judgment. For example, generalizing from observations made on the mental health status of a sample of lawyers in Delhi to the mental health status of all lawyers in Delhi is a formalized procedure, in so far as the errors (sampling or random) which this may hazard can, to some extent, be calculated in advance. However, if we attempt to generalize further, for instance, about the mental statuses of all lawyers in the country as a whole, we hazard further pitfalls which cannot be specified in advance. We do not know to what extent the study sample and population of Delhi is typical of the larger population – that of the whole country – to which it belongs.

The dilemmas in defining populations differ for descriptive and analytic studies.

POPULATION IN DESCRIPTIVE STUDIES

In descriptive studies, it is customary to define a study population and then make observations on a sample taken from it. Study populations may be defined by geographic location, age, sex, with additional definitions of attributes and variables such as occupation, religion and ethnic group.[ 1 ]

Geographic location

In field studies, it may be desirable to use a population defined by an administrative boundary such as a district or a state. This may facilitate the co-operation of the local administrative authorities and the study participants. Moreover, basic demographic data on the population such as population size, age, gender distribution (needed for calculating age- and sex-specific rates) available from census data or voters’ list are easier to obtain from administrative headquarters. However, administrative boundaries do not always consist of homogenous group of people. Since it is desirable that a modest descriptive study does not cover a number of different groups of people, with widely differing ways of life or customs, it may be necessary to restrict the study to a particular ethnic group, and thus ensure better genetic or cultural homogeneity. Alternatively, a population may be defined in relation to a prominent geographic feature, such as a river, or mountain, which imposes a certain uniformity of ways of life, attitudes, and behavior upon the people who live in the vicinity.

If cases of a disease are being ascertained through their attendance at a hospital outpatient department (OPD), rather than by field surveys in the community, it will be necessary to define the population according to the so-called catchment area of the hospital OPD. For administrative purposes, a dispensary, health center or hospital is usually considered to serve a population within a defined geographic area. But these catchment areas may only represent in a crude manner with the actual use of medical facilities by the local people. For example, in OPD study of psychiatric illnesses in a particular hospital with a defined catchment area, many people with psychiatric illnesses may not visit the particular OPD and may seek treatment from traditional healers or religious leaders.

Catchment areas depend on the demography of the area and the accessibility of the health center or hospital. Accessibility has three dimensions – physical, economic and social.[ 2 ] Physical accessibility is the time required to travel to the health center or medical facility. It depends on the topography of the area (e.g. hill and tribal areas with poor roads have problems of physical accessibility). Economic accessibility is the paying capacity of the people for services. Poverty may limit health seeking behavior if the person cannot afford the bus fare to the health center even if the health services may be free of charge. It may also involve absence from work which, for daily wage earners, is a major economic disincentive. Social factors such as caste, culture, language, etc. may adversely affect accessibility to health facility if the treating physician is not conversant with the local language and customs. In such situations, the patient may feel more comfortable with traditional healers.

Ascertainment of a particular disease within a particular area may be incomplete either because some patient may seek treatment elsewhere or some patients do not seek treatment at all. Focus group discussions (qualitative study) with local people, especially those residing away from the health center, may give an indication whether serious underreporting is occurring.

When it is impossible to relate cases of a disease to a population, perhaps because the cases were ascertained through a hospital with an undefined catchment area, proportional morbidity rates may be used. These rates have been widely used in cancer epidemiology where the number of cases of one form of cancer is expressed as a proportion of the number of cases of all forms of cancer among patients attending the same hospital during the same period.

POPULATIONS IN ANALYTIC STUDIES

Case control studies.

As opposed to descriptive studies where a study population is defined and then observations are made on a representative sample from it, in case control studies observations are made on a group of patients. This is known as the study group , which usually is not selected by sampling of a defined larger group. For instance, a study on patients of bipolar disorder may include every patient with this disorder attending the psychiatry OPD during the study period. One should not forget, however, that in this situation also, there is a hypothetical population consisting of all patients with bipolar disorder in the universe (which may be a certain region, a country or globally depending on the extent of the generalization intended from the findings of the study). Case control studies are often carried out in hospital settings because this is more convenient and accessible group than cases in the community at large. However, the two groups of cases may differ in many respects. At the outset of the study, it should be deliberated whether these differences would affect the external validity (generalization) of the study. Usually, analytic studies are not carried out in groups containing atypical cases of the disorder, unless there is a special indication to do so.

Populations in cohort studies

Basically, cohort studies compare two groups of people (cohorts) and demonstrate whether or not there are more cases of the disease among the cohort exposed to the suspected cause than among the cohort not exposed. To determine whether an association exists between positive family history of schizophrenia and subsequent schizophrenia in persons having such a history, two cohorts would be required: first, the exposed group, that is, people with a family history of mental disorders (the suspected cause) and second, the unexposed group, that is, people without a family history of mental disorders. These two cohorts would need to be followed up for a number of years and cases of schizophrenia in either group would be recorded. If a positive family history is associated with development of schizophrenia, then more cases would occur in the first group than in the second group.

The crucial challenges in a cohort study are that it should include participants exposed to a particular cause being investigated and that it should consist of persons who can be followed up for the period of time between exposure (cause) and development of the disorder. It is vital that the follow-up of a cohort should be complete as far as possible. If more than a small proportion of persons in the cohort cannot be traced (loss to follow-up or attrition), the findings will be biased , in case these persons differ significantly from those remaining in the study.

Depending on the type of exposure being studied, there may or may not be a range of choice of cohort populations exposed to it who may form a larger population from which one has to select a study sample. For instance, if one is exploring association between occupational hazard such as job stress in health care workers in intensive care units (ICUs) and subsequent development of drug addiction, one has to, by the very nature of the research question, select health care workers working in ICUs. On the other hand, cause effect study for association between head injury and epilepsy offers a much wider range of possible cohorts.

Difficulties in making repeated observations on cohorts depend on the length of time of the study. In correlating maternal factors (pregnancy cohort) with birth weight, the period of observation is limited to 9 months. However, if in a study it is tried to find the association between maternal nutrition during pregnancy and subsequent school performance of the child, the study will extend to years. For such long duration investigations, it is wise to select study cohorts that are firstly, not likely to migrate, cooperative and likely to be so throughout the duration of the study, and most importantly, easily accessible to the investigator so that the expense and efforts are kept within reasonable limits. Occupational groups such as the armed forces, railways, police, and industrial workers are ideal for cohort studies. Future developments facilitating record linkage such as the Unique Identification Number Scheme may give a boost to cohort studies in the wider community.

A sample is any part of the fully defined population. A syringe full of blood drawn from the vein of a patient is a sample of all the blood in the patient's circulation at the moment. Similarly, 100 patients of schizophrenia in a clinical study is a sample of the population of schizophrenics, provided the sample is properly chosen and the inclusion and exclusion criteria are well defined.

To make accurate inferences, the sample has to be representative. A representative sample is one in which each and every member of the population has an equal and mutually exclusive chance of being selected.

Sample size

Inputs required for sample size calculation have been dealt from a clinical researcher's perspective avoiding the use of intimidating formulae and statistical jargon in an earlier issue of the journal.[ 1 ]

Target population, study population and study sample

A population is a complete set of people with a specialized set of characteristics, and a sample is a subset of the population. The usual criteria we use in defining population are geographic, for example, “the population of Uttar Pradesh”. In medical research, the criteria for population may be clinical, demographic and time related.

• Clinical and demographic characteristics define the target population, the large set of people in the world to which the results of the study will be generalized (e.g. all schizophrenics).
• The study population is the subset of the target population available for study (e.g. schizophrenics in the researcher's town).
• The study sample is the sample chosen from the study population.

METHODS OF SAMPLING

Purposive (non-random samples).

• Volunteers who agree to participate
• Snowball sample, where one case identifies others of his kind (e.g. intravenous drug users)
• Convenient sample such as captive medical students or other readily available groups
• Quota sampling, at will selection of a fixed number from each group
• Referred cases who may be under pressure to participate
• Haphazard with combination of the above methods

Non-random samples have certain limitations. The larger group (target population) is difficult to identify. This may not be a limitation when generalization of results is not intended. The results would be valid for the sample itself (internal validity). They can, nevertheless, provide important clues for further studies based on random samples. Another limitation of non-random samples is that statistical inferences such as confidence intervals and tests of significance cannot be estimated from non-random samples. However, in some situations, the investigator has to make crucial judgments. One should remember that random samples are the means but representativeness is the goal. When non-random samples are representative (compare the socio-demographic characteristics of the sample subjects with the target population), generalization may be possible.

Random sampling methods

Simple random sampling.

A sample may be defined as random if every individual in the population being sampled has an equal likelihood of being included. Random sampling is the basis of all good sampling techniques and disallows any method of selection based on volunteering or the choice of groups of people known to be cooperative.[ 3 ]

In order to select a simple random sample from a population, it is first necessary to identify all individuals from whom the selection will be made. This is the sampling frame. In developing countries, listings of all persons living in an area are not usually available. Census may not catch nomadic population groups. Voters’ and taxpayers’ lists may be incomplete. Whether or not such deficiencies are major barriers in random sampling depends on the particular research question being investigated. To undertake a separate exercise of listing the population for the study may be time consuming and tedious. Two-stage sampling may make the task feasible.

The usual method of selecting a simple random sample from a listing of individuals is to assign a number to each individual and then select certain numbers by reference to random number tables which are published in standard statistical textbooks. Random number can also be generated by statistical software such as EPI INFO developed by WHO and CDC Atlanta.

Systematic sampling

A simple method of random sampling is to select a systematic sample in which every n th person is selected from a list or from other ordering. A systematic sample can be drawn from a queue of people or from patients ordered according to the time of their attendance at a clinic. Thus, a sample can be drawn without an initial listing of all the subjects. Because of this feasibility, a systematic sample may have some advantage over a simple random sample.

To fulfill the statistical criteria for a random sample, a systematic sample should be drawn from subjects who are randomly ordered. The starting point for selection should be randomly chosen. If every fifth person from a register is being chosen, then a random procedure must be used to determine whether the first, second, third, fourth, or fifth person should be chosen as the first member of the sample.

Multistage sampling

Sometimes, a strictly random sample may be difficult to obtain and it may be more feasible to draw the required number of subjects in a series of stages. For example, suppose we wish to estimate the number of CATSCAN examinations made of all patients entering a hospital in a given month in the state of Maharashtra. It would be quite tedious to devise a scheme which would allow the total population of patients to be directly sampled. However, it would be easier to list the districts of the state of Maharashtra and randomly draw a sample of these districts. Within this sample of districts, all the hospitals would then be listed by name, and a random sample of these can be drawn. Within each of these hospitals, a sample of the patients entering in the given month could be chosen randomly for observation and recording. Thus, by stages, we draw the required sample. If indicated, we can introduce some element of stratification at some stage (urban/rural, gender, age).

It should be cautioned that multistage sampling should only be resorted to when difficulties in simple random sampling are insurmountable. Those who take a simple random sample of 12 hospitals, and within each of these hospitals select a random sample of 10 patients, may believe they have selected 120 patients randomly from all the 12 hospitals. In statistical sense, they have in fact selected a sample of 12 rather than 120.[ 4 ]

Stratified sampling

If a condition is unevenly distributed in a population with respect to age, gender, or some other variable, it may be prudent to choose a stratified random sampling method. For example, to obtain a stratified random sample according to age, the study population can be divided into age groups such as 0–5, 6–10, 11–14, 15–20, 21–25, and so on, depending on the requirement. A different proportion of each group can then be selected as a subsample either by simple random sampling or systematic sampling. If the condition decreases with advancing age, then to include adequate number in the older age groups, one may select more numbers in older subsamples.

Cluster sampling

In many surveys, studies may be carried out on large populations which may be geographically quite dispersed. To obtain the required number of subjects for the study by a simple random sample method will require large costs and will be cumbersome. In such cases, clusters may be identified (e.g. households) and random samples of clusters will be included in the study; then, every member of the cluster will also be part of the study. This introduces two types of variations in the data – between clusters and within clusters – and this will have to be taken into account when analyzing data.

Cluster sampling may produce misleading results when the disease under study itself is distributed in a clustered fashion in an area. For example, suppose we are studying malaria in a population. Malaria incidence may be clustered in villages having stagnant water collections which may serve as a source of mosquito breeding. In villages without such water stagnation, there will be lesser malaria cases. The choice of few villages in cluster sampling may give erroneous results. The selection of villages as a cluster may be quite unrepresentative of the whole population by chance.[ 5 ]

Lot quality assurance sampling

Lot quality assurance sampling (LQAS), which originated in the manufacturing industry for quality control purposes, was used in the nineties to assess immunization coverage, estimate disease prevalence, and evaluate control measures and service coverage in different health programs.[ 6 ] Using only a small sample size, LQAS can effectively differentiate between areas that have or have not met the performance targets. Thus, this method is used not only to estimate the coverage of quality care but also to identify the exact subdivisions where it is deficient so that appropriate remedial measures can be implemented.

The choice of sampling methods is usually dictated by feasibility in terms of time and resources. Field research is quite messy and difficult like actual battle. It may be sometimes difficult to get a sample which is truly random. Most samples therefore tend to get biased. To estimate the magnitude of this bias, the researcher should have some idea about the population from which the sample is drawn. In conclusion, the following quote cited by Bradford Hill[ 4 ] elegantly sums up the benefit of random sampling:

…The actual practice of medicine is virtually confined to those members of the population who either are ill, or think they are ill, or are thought by somebody to be ill, and these so amply fill up the working day that in the course of time one comes unconsciously to believe that they are typical of the whole. This is not the case. The use of a random sample brings to light the individuals who are ill and know they are ill but have no intention of doing anything about it, as well as those who have never been ill, and probably never will be until their final illness. These would have been inaccessible to any other method of approach but that of the random sample… . J. H. Sheldon

Source of Support: Nil.

Conflict of Interest: None declared.

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1.2: Samples vs. Populations

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• Page ID 24019

• Rachel Webb
• Portland State University

The first thing to decide in a statistical study is whom you want to measure and what you want to measure. You always want to make sure that you can answer the question of whom you measured and what you measured. The “who” is known as the individual and the “what” is known as the variable.

Individual – a person, case or object that you are interested in finding out information about.

Variable (also known as a random variable) – the measurement or observation of the individual.

Population – is the total set of all the observations that are the subject of a study.

Notice, the population answers “who” you want to measure and the variable answers “what” you want to measure. Make sure that you always answer both of these questions or you have not given the audience reading your study the entire picture. As an example, if you just say that you are going to collect data from the senators in the United States Congress, you have not told your reader what you are going to collect. Do you want to know their income, their highest degree earned, their voting record, their age, their political party, their gender, their marital status, or how they feel about a particular issue? Without telling “what” you what to measure, your reader has no idea what your study is actually about.

Sometimes the population is very easy to collect. If you are interested in finding the average age of all of the current senators in the United States Congress, there are only 100 senators. This would not be hard to find. However, if instead you were interested in knowing the average age that a senator in the United States Congress first took office for all senators that ever served in the United States Congress, then this would be a bit more work. It is still doable, but it would take a bit of time to collect. However, what if you are interested in finding the average diameter at breast height of all Ponderosa Pine trees in the Coconino National Forest? This data would be impossible to collect. What do you do in these cases? Instead of collecting the entire population, you take a smaller group of the population, a snapshot of the population. This smaller group, called a sample, is a subset of the population, see Figure 1-1.

Sample – a subset from the population.

Consider the following three research questions:

• What is the average mercury content in albacore tuna in the Pacific Ocean?
• Over the last 5 years, what is the average time to complete a degree for Portland State University undergraduate students?
• Does a new drug reduce the number of deaths in patients with severe heart disease?

Each research question refers to a target population. In the first question, the target population is all albacore tuna in the Pacific Ocean, and each fish represents a case.

A sample represents a subset of the cases and is often a small fraction of the population. For instance, 60 albacore tuna in the population might be selected and the mercury level is measured in each fish. The sample average of the 60 fish may then be used to provide an estimate of the population average of all the fish and answer the research question.

We use the lower-case n to represent the number of cases in the sample and the upper-case N to represent the number of cases in the population.

n = sample size.

N = population size.

How the sample is collected can determine the accuracy of the results of your study. There are many ways to collect samples. No sampling method is perfect, but some methods are better than other methods. Sampling techniques will be discussed in more detail later.

For now, realize that every time you take a sample you will find different data values. The sample is a snapshot of the population, and there is more information than is in this small picture. The idea is to try to collect a sample that gives you an accurate picture, but you will never know for sure if your picture is the correct picture. Unlike previous mathematics classes, where there was always one right answer, in statistics there can be many answers, and you do not know which are right.

The sample average in this case is the statistic, and the population average is the parameter. We use sample statistics to make inferences, educated guesses made by observation, about the population parameter.

Once you have your data, either from a population or from a sample, you need to know how you want to summarize the data.

As an example, suppose you are interested in finding the proportion of people who like a candidate, the average height a plant grows to using a new fertilizer, or the variability of the test scores. Understanding how you want to summarize the data helps to determine the type of data you want to collect. Since the population is what we are interested in, then you want to calculate a number from the population. This is known as a parameter.

Parameter – An unknown quantity from the population. Usually denoted with a Greek letter (for example μ “mu”). This number is a fixed, unknown number that we want to estimate.

As mentioned already, it is hard to collect the entire population. Even though this is the number you are interested in, you cannot really calculate it. Instead, you use the number calculated from the sample, called a statistic, to estimate the parameter.

Statistic – a number calculated from the sample. Usually denoted with a ^ (called a hat, for example \(\hat{p}\) “p-hat”) or a – (called a bar, for example \(\bar{x}\) “x-bar”) above the letter.

Since most samples are not exactly the same, the statistic values are going to be different from sample to sample. Statistics estimate the value of the parameter, but again, you do not know for sure if your statistic is correctly estimating the parameter.

3. Populations and samples

Populations, unbiasedness and precision, randomisation, variation between samples, standard error of the mean.

Statistics Made Easy

Population vs. Sample: What’s the Difference?

Often in statistics we’re interested in collecting data so that we can answer some research question.

For example, we might want to answer the following questions:

1. What is the median household income in Miami, Florida?

2. What is the mean weight of a certain population of turtles?

3. What percentage of residents in a certain county support a certain law?

In each scenario, we are interested in answering some question about a  population , which represents every possible individual element that we’re interested in measuring.

However, instead of collecting data on every individual in a population we instead collect data on a sample of the population, which represents a portion of the population.

Population : Every possible individual element that we are interested in measuring.   Sample: A portion of the population.

Here is an example of a population vs. a sample in the three intro examples.

Example 1: What is the median household income in Miami, Florida?

The entire population might include 500,000 households, but we might only collect data on a sample of 2,000 total households.

The entire population might include 800 turtles, but we might only collect data on a sample of 30 turtles.

The entire population might include 50,000 residents, but we might only collect data on a sample of 1,000 residents.

Why Use Samples?

There are several reasons that we typically collect data on samples instead of entire populations, including:

1 . It is too time-consuming to collect data on an entire population . For example, if we want to know the median household income in Miami, Florida, it might take months or even years to go around and gather income for each household. By the time we collect all of this data, the population may have changed or the research question of interest might no longer be of interest.

2. It is too costly to collect data on an entire population. It is often too expensive to go around and collect data for every individual in a population, which is why we instead choose to collect data on a sample instead.

3. It is unfeasible to collect data on an entire population. In many cases it’s simply not possible to collect data for  every individual in a population. For example, it may be extraordinarily difficult to track down and weigh every turtle in a certain population that we’re interested in. 

By collecting data on samples, we’re able to gather information about a given population much faster and cheaper.

And if our sample is  representative of the population , then we can generalize the findings from a sample to the larger population with a high level of confidence.

The Importance of Representative Samples

When we collect a sample from a population, we ideally want the sample to be like a “mini version” of our population.

For example, suppose we want to understand the movie preferences of students in a certain school district that has a population of 5,000 total students. Since it would take too long to survey every individual student, we might instead take a sample of 100 students and ask them about their preferences. 

If the overall student population is composed of 50% girls and 50% boys, our sample would not be representative if it included 90% boys and only 10% girls.

Or if the overall population is composed of equal parts freshman, sophomores, juniors, and seniors, then our sample would not be representative if it only included freshman. 

A sample is representative of a population if the characteristics of the individuals in the sample closely matches the characteristics of the individuals in the overall population.

When this occurs, we can generalize the findings from the sample to the overall population with confidence. 

How to Obtain Samples

There are many different methods we can use to obtain samples from populations. 

To maximize the chances that we obtain a representative sample, we can use one of the three following methods:

Simple random sampling: Randomly select individuals through the use of a random number generator or some means of random selection.

Systematic random sampling: Put every member of a population into some order. Choose a random starting point and select every n th member to be in the sample.

Stratified random sampling: Split a population into groups. Randomly select some members from each group to be in the sample. 

In each of these methods, every individual in the population has an equal probability of being included in the sample. This maximizes the chances that we obtain a sample that is a “mini version” of the population.

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Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

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simple but very accurate explanation

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Introduction to Research Methods

7 samples and populations.

So you’ve developed your research question, figured out how you’re going to measure whatever you want to study, and have your survey or interviews ready to go. Now all your need is other people to become your data.

You might say ‘easy!’, there’s people all around you. You have a big family tree and surely them and their friends would have happy to take your survey. And then there’s your friends and people you’re in class with. Finding people is way easier than writing the interview questions or developing the survey. That reaction might be a strawman, maybe you’ve come to the conclusion none of this is easy. For your data to be valuable, you not only have to ask the right questions, you have to ask the right people. The “right people” aren’t the best or the smartest people, the right people are driven by what your study is trying to answer and the method you’re using to answer it.

Remember way back in chapter 2 when we looked at this chart and discussed the differences between qualitative and quantitative data.

One of the biggest differences between quantitative and qualitative data was whether we wanted to be able to explain something for a lot of people (what percentage of residents in Oklahoma support legalizing marijuana?) versus explaining the reasons for those opinions (why do some people support legalizing marijuana and others not?). The underlying differences there is whether our goal is explain something about everyone, or whether we’re content to explain it about just our respondents.

‘Everyone’ is called the population . The population in research is whatever group the research is trying to answer questions about. The population could be everyone on planet Earth, everyone in the United States, everyone in rural counties of Iowa, everyone at your university, and on and on. It is simply everyone within the unit you are intending to study.

In order to study the population, we typically take a sample or a subset. A sample is simply a smaller number of people from the population that are studied, which we can use to then understand the characteristics of the population based on that subset. That’s why a poll of 1300 likely voters can be used to guess at who will win your states Governor race. It isn’t perfect, and we’ll talk about the math behind all of it in a later chapter, but for now we’ll just focus on the different types of samples you might use to study a population with a survey.

If correctly sampled, we can use the sample to generalize information we get to the population. Generalizability , which we defined earlier, means we can assume the responses of people to our study match the responses everyone would have given us. We can only do that if the sample is representative of the population, meaning that they are alike on important characteristics such as race, gender, age, education. If something makes a large difference in people’s views on a topic in your research and your sample is not balanced, you’ll get inaccurate results.

Generalizability is more of a concern with surveys than with interviews. The goal of a survey is to explain something about people beyond the sample you get responses from. You’ll never see a news headline saying that “53% of 1250 Americans that responded to a poll approve of the President”. It’s only worth asking those 1250 people if we can assume the rest of the United States feels the same way overall. With interviews though we’re looking for depth from their responses, and so we are less hopefully that the 15 people we talk to will exactly match the American population. That doesn’t mean the data we collect from interviews doesn’t have value, it just has different uses.

There are two broad types of samples, with several different techniques clustered below those. Probability sampling is associated with surveys, and non-probability sampling is often used when conducting interviews. We’ll first describe probability samples, before discussing the non-probability options.

The type of sampling you’ll use will be based on the type of research you’re intending to do. There’s no sample that’s right or wrong, they can just be more or less appropriate for the question you’re trying to answer. And if you use a less appropriate sampling strategy, the answer you get through your research is less likely to be accurate.

7.1 Types of Probability Samples

So we just hinted at the idea that depending on the sample you use, you can generalize the data you collect from the sample to the population. That will depend though on whether your sample represents the population. To ensure that your sample is representative of the population, you will want to use a probability sample. A representative sample refers to whether the characteristics (race, age, income, education, etc) of the sample are the same as the population. Probability sampling is a sampling technique in which every individual in the population has an equal chance of being selected as a subject for the research.

There are several different types of probability samples you can use, depending on the resources you have available.

Let’s start with a simple random sample . In order to use a simple random sample all you have to do is take everyone in your population, throw them in a hat (not literally, you can just throw their names in a hat), and choose the number of names you want to use for your sample. By drawing blindly, you can eliminate human bias in constructing the sample and your sample should represent the population from which it is being taken.

However, a simple random sample isn’t quite that easy to build. The biggest issue is that you have to know who everyone is in order to randomly select them. What that requires is a sampling frame , a list of all residents in the population. But we don’t always have that. There is no list of residents of New York City (or any other city). Organizations that do have such a list wont just give it away. Try to ask your university for a list and contact information of everyone at your school so you can do a survey? They wont give it to you, for privacy reasons. It’s actually harder to think of popultions you could easily develop a sample frame for than those you can’t. If you can get or build a sampling frame, the work of a simple random sample is fairly simple, but that’s the biggest challenge.

Most of the time a true sampling frame is impossible to acquire, so researcher have to settle for something approximating a complete list. Earlier generations of researchers could use the random dial method to contact a random sample of Americans, because every household had a single phone. To use it you just pick up the phone and dial random numbers. Assuming the numbers are actually random, anyone might be called. That method actually worked somewhat well, until people stopped having home phone numbers and eventually stopped answering the phone. It’s a fun mental exercise to think about how you would go about creating a sampling frame for different groups though; think through where you would look to find a list of everyone in these groups:

Plumbers Recent first-time fathers Members of gyms

The best way to get an actual sampling frame is likely to purchase one from a private company that buys data on people from all the different websites we use.

Let’s say you do have a sampling frame though. For instance, you might be hired to do a survey of members of the Republican Party in the state of Utah to understand their political priorities this year, and the organization could give you a list of their members because they’ve hired you to do the reserach. One method of constructing a simple random sample would be to assign each name on the list a number, and then produce a list of random numbers. Once you’ve matched the random numbers to the list, you’ve got your sample. See the example using the list of 20 names below

and the list of 5 random numbers.

Systematic sampling is similar to simple random sampling in that it begins with a list of the population, but instead of choosing random numbers one would select every kth name on the list. What the heck is a kth? K just refers to how far apart the names are on the list you’re selecting. So if you want to sample one-tenth of the population, you’d select every tenth name. In order to know the k for your study you need to know your sample size (say 1000) and the size of the population (75000). You can divide the size of the population by the sample (75000/1000), which will produce your k (750). As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method, but its only advantage over the random sampling technique is simplicity. If we used the same list as above and wanted to survey 1/5th of the population, we’d include 4 of the names on the list. It’s important with systematic samples to randomize the starting point in the list, otherwise people with A names will be oversampled. If we started with the 3rd name, we’d select Annabelle Frye, Cristobal Padilla, Jennie Vang, and Virginia Guzman, as shown below. So in order to use a systematic sample, we need three things, the population size (denoted as N ), the sample size we want ( n ) and k , which we calculate by dividing the population by the sample).

N= 20 (Population Size) n= 4 (Sample Size) k= 5 {20/4 (kth element) selection interval}

We can also use a stratified sample , but that requires knowing more about the population than just their names. A stratified sample divides the study population into relevant subgroups, and then draws a sample from each subgroup. Stratified sampling can be used if you’re very concerned about ensuring balance in the sample or there may be a problem of underrepresentation among certain groups when responses are received. Not everyone in your sample is equally likely to answer a survey. Say for instance we’re trying to predict who will win an election in a county with three cities. In city A there are 1 million college students, in city B there are 2 million families, and in City C there are 3 million retirees. You know that retirees are more likely than busy college students or parents to respond to a poll. So you break the sample into three parts, ensuring that you get 100 responses from City A, 200 from City B, and 300 from City C, so the three cities would match the population. A stratified sample provides the researcher control over the subgroups that are included in the sample, whereas simple random sampling does not guarantee that any one type of person will be included in the final sample. A disadvantage is that it is more complex to organize and analyze the results compared to simple random sampling.

Cluster sampling is an approach that begins by sampling groups (or clusters) of population elements and then selects elements from within those groups. A researcher would use cluster sampling if getting access to elements in an entrie population is too challenging. For instance, a study on students in schools would probably benefit from randomly selecting from all students at the 36 elementary schools in a fictional city. But getting contact information for all students would be very difficult. So the researcher might work with principals at several schools and survey those students. The researcher would need to ensure that the students surveyed at the schools are similar to students throughout the entire city, and greater access and participation within each cluster may make that possible.

The image below shows how this can work, although the example is oversimplified. Say we have 12 students that are in 6 classrooms. The school is in total 1/4th green (3/12), 1/4th yellow (3/12), and half blue (6/12). By selecting the right clusters from within the school our sample can be representative of the entire school, assuming these colors are the only significant difference between the students. In the real world, you’d want to match the clusters and population based on race, gender, age, income, etc. And I should point out that this is an overly simplified example. What if 5/12s of the school was yellow and 1/12th was green, how would I get the right proportions? I couldn’t, but you’d do the best you could. You still wouldn’t want 4 yellows in the sample, you’d just try to approximiate the population characteristics as best you can.

7.2 Actually Doing a Survey

All of that probably sounds pretty complicated. Identifying your population shouldn’t be too difficult, but how would you ever get a sampling frame? And then actually identifying who to include… It’s probably a bit overwhelming and makes doing a good survey sound impossible.

Researchers using surveys aren’t superhuman though. Often times, they use a little help. Because surveys are really valuable, and because researchers rely on them pretty often, there has been substantial growth in companies that can help to get one’s survey to its intended audience.

One popular resource is Amazon’s Mechanical Turk (more commonly known as MTurk). MTurk is at its most basic a website where workers look for jobs (called hits) to be listed by employers, and choose whether to do the task or not for a set reward. MTurk has grown over the last decade to be a common source of survey participants in the social sciences, in part because hiring workers costs very little (you can get some surveys completed for penny’s). That means you can get your survey completed with a small grant ($1-2k at the low end) and get the data back in a few hours. Really, it’s a quick and easy way to run a survey.

However, the workers aren’t perfectly representative of the average American. For instance, researchers have found that MTurk respondents are younger, better educated, and earn less than the average American.

One way to get around that issue, which can be used with MTurk or any survey, is to weight the responses. Because with MTurk you’ll get fewer responses from older, less educated, and richer Americans, those responses you do give you want to count for more to make your sample more representative of the population. Oversimplified example incoming!

Imagine you’re setting up a pizza party for your class. There are 9 people in your class, 4 men and 5 women. You only got 4 responses from the men, and 3 from the women. All 4 men wanted peperoni pizza, while the 3 women want a combination. Pepperoni wins right, 4 to 3? Not if you assume that the people that didn’t respond are the same as the ones that did. If you weight the responses to match the population (the full class of 9), a combination pizza is the winner.

Because you know the population of women is 5, you can weight the 3 responses from women by 5/3 = 1.6667. If we weight (or multiply) each vote we did receive from a woman by 1.6667, each vote for a combination now equals 1.6667, meaning that the 3 votes for combination total 5. Because we received a vote from every man in the class, we just weight their votes by 1. The big assumption we have to make is that the people we didn’t hear from (the 2 women that didn’t vote) are similar to the ones we did hear from. And if we don’t get any responses from a group we don’t have anything to infer their preferences or views from.

Let’s go through a slightly more complex example, still just considering one quality about people in the class. Let’s say your class actually has 100 students, but you only received votes from 50. And, what type of pizza people voted for is mixed, but men still prefer peperoni overall, and women still prefer combination. The class is 60% female and 40% male.

We received 21 votes from women out of the 60, so we can weight their responses by 60/21 to represent the population. We got 29 votes out of the 40 for men, so their responses can be weighted by 40/29. See the math below.

53.8 votes for combination? That might seem a little odd, but weighting isn’t a perfect science. We can’t identify what a non-respondent would have said exactly, all we can do is use the responses of other similar people to make a good guess. That issue often comes up in polling, where pollsters have to guess who is going to vote in a given election in order to project who will win. And we can weight on any characteristic of a person we think will be important, alone or in combination. Modern polls weight on age, gender, voting habits, education, and more to make the results as generalizable as possible.

There’s an appendix later in this book where I walk through the actual steps of creating weights for a sample in R, if anyone actually does a survey. I intended this section to show that doing a good survey might be simpler than it seemed, but now it might sound even more difficult. A good lesson to take though is that there’s always another door to go through, another hurdle to improve your methods. Being good at research just means being constantly prepared to be given a new challenge, and being able to find another solution.

7.3 Non-Probability Sampling

Qualitative researchers’ main objective is to gain an in-depth understanding on the subject matter they are studying, rather than attempting to generalize results to the population. As such, non-probability sampling is more common because of the researchers desire to gain information not from random elements of the population, but rather from specific individuals.

Random selection is not used in nonprobability sampling. Instead, the personal judgment of the researcher determines who will be included in the sample. Typically, researchers may base their selection on availability, quotas, or other criteria. However, not all members of the population are given an equal chance to be included in the sample. This nonrandom approach results in not knowing whether the sample represents the entire population. Consequently, researchers are not able to make valid generalizations about the population.

As with probability sampling, there are several types of non-probability samples. Convenience sampling , also known as accidental or opportunity sampling, is a process of choosing a sample that is easily accessible and readily available to the researcher. Researchers tend to collect samples from convenient locations such as their place of employment, a location, school, or other close affiliation. Although this technique allows for quick and easy access to available participants, a large part of the population is excluded from the sample.

For example, researchers (particularly in psychology) often rely on research subjects that are at their universities. That is highly convenient, students are cheap to hire and readily available on campuses. However, it means the results of the study may have limited ability to predict motivations or behaviors of people that aren’t included in the sample, i.e., people outside the age of 18-22 that are going to college.

If I ask you to get find out whether people approve of the mayor or not, and tell you I want 500 people’s opinions, should you go stand in front of the local grocery store? That would be convinient, and the people coming will be random, right? Not really. If you stand outside a rural Piggly Wiggly or an urban Whole Foods, do you think you’ll see the same people? Probably not, people’s chracteristics make the more or less likely to be in those locations. This technique runs the high risk of over- or under-representation, biased results, as well as an inability to make generalizations about the larger population. As the name implies though, it is convenient.

Purposive sampling , also known as judgmental or selective sampling, refers to a method in which the researcher decides who will be selected for the sample based on who or what is relevant to the study’s purpose. The researcher must first identify a specific characteristic of the population that can best help answer the research question. Then, they can deliberately select a sample that meets that particular criterion. Typically, the sample is small with very specific experiences and perspectives. For instance, if I wanted to understand the experiences of prominent foreign-born politicians in the United States, I would purposefully build a sample of… prominent foreign-born politicians in the United States. That would exclude anyone that was born in the United States or and that wasn’t a politician, and I’d have to define what I meant by prominent. Purposive sampling is susceptible to errors in judgment by the researcher and selection bias due to a lack of random sampling, but when attempting to research small communities it can be effective.

When dealing with small and difficult to reach communities researchers sometimes use snowball samples , also known as chain referral sampling. Snowball sampling is a process in which the researcher selects an initial participant for the sample, then asks that participant to recruit or refer additional participants who have similar traits as them. The cycle continues until the needed sample size is obtained.

This technique is used when the study calls for participants who are hard to find because of a unique or rare quality or when a participant does not want to be found because they are part of a stigmatized group or behavior. Examples may include people with rare diseases, sex workers, or a child sex offenders. It would be impossible to find an accurate list of sex workers anywhere, and surveying the general population about whether that is their job will produce false responses as people will be unwilling to identify themselves. As such, a common method is to gain the trust of one individual within the community, who can then introduce you to others. It is important that the researcher builds rapport and gains trust so that participants can be comfortable contributing to the study, but that must also be balanced by mainting objectivity in the research.

Snowball sampling is a useful method for locating hard to reach populations but cannot guarantee a representative sample because each contact will be based upon your last. For instance, let’s say you’re studying illegal fight clubs in your state. Some fight clubs allow weapons in the fights, while others completely ban them; those two types of clubs never interreact because of their disagreement about whether weapons should be allowed, and there’s no overlap between them (no members in both type of club). If your initial contact is with a club that uses weapons, all of your subsequent contacts will be within that community and so you’ll never understand the differences. If you didn’t know there were two types of clubs when you started, you’ll never even know you’re only researching half of the community. As such, snowball sampling can be a necessary technique when there are no other options, but it does have limitations.

Quota Sampling is a process in which the researcher must first divide a population into mutually exclusive subgroups, similar to stratified sampling. Depending on what is relevant to the study, subgroups can be based on a known characteristic such as age, race, gender, etc. Secondly, the researcher must select a sample from each subgroup to fit their predefined quotas. Quota sampling is used for the same reason as stratified sampling, to ensure that your sample has representation of certain groups. For instance, let’s say that you’re studying sexual harassment in the workplace, and men are much more willing to discuss their experiences than women. You might choose to decide that half of your final sample will be women, and stop requesting interviews with men once you fill your quota. The core difference is that while stratified sampling chooses randomly from within the different groups, quota sampling does not. A quota sample can either be proportional or non-proportional . Proportional quota sampling refers to ensuring that the quotas in the sample match the population (if 35% of the company is female, 35% of the sample should be female). Non-proportional sampling allows you to select your own quota sizes. If you think the experiences of females with sexual harassment are more important to your research, you can include whatever percentage of females you desire.

7.4 Dangers in sampling

Now that we’ve described all the different ways that one could create a sample, we can talk more about the pitfalls of sampling. Ensuring a quality sample means asking yourself some basic questions:

• Who is in the sample?
• How were they sampled?
• Why were they sampled?

A meal is often only as good as the ingredients you use, and your data will only be as good as the sample. If you collect data from the wrong people, you’ll get the wrong answer. You’ll still get an answer, it’ll just be inaccurate. And I want to reemphasize here wrong people just refers to inappropriate for your study. If I want to study bullying in middle schools, but I only talk to people that live in a retirement home, how accurate or relevant will the information I gather be? Sure, they might have grandchildren in middle school, and they may remember their experiences. But wouldn’t my information be more relevant if I talked to students in middle school, or perhaps a mix of teachers, parents, and students? I’ll get an answer from retirees, but it wont be the one I need. The sample has to be appropriate to the research question.

Is a bigger sample always better? Not necessarily. A larger sample can be useful, but a more representative one of the population is better. That was made painfully clear when the magazine Literary Digest ran a poll to predict who would win the 1936 presidential election between Alf Landon and incumbent Franklin Roosevelt. Literary Digest had run the poll since 1916, and had been correct in predicting the outcome every time. It was the largest poll ever, and they received responses for 2.27 million people. They essentially received responses from 1 percent of the American population, while many modern polls use only 1000 responses for a much more populous country. What did they predict? They showed that Alf Landon would be the overwhelming winner, yet when the election was held Roosevelt won every state except Maine and Vermont. It was one of the most decisive victories in Presidential history.

So what went wrong for the Literary Digest? Their poll was large (gigantic!), but it wasn’t representative of likely voters. They polled their own readership, which tended to be more educated and wealthy on average, along with people on a list of those with registered automobiles and telephone users (both of which tended to be owned by the wealthy at that time). Thus, the poll largely ignored the majority of Americans, who ended up voting for Roosevelt. The Literary Digest poll is famous for being wrong, but led to significant improvements in the science of polling to avoid similar mistakes in the future. Researchers have learned a lot in the century since that mistake, even if polling and surveys still aren’t (and can’t be) perfect.

What kind of sampling strategy did Literary Digest use? Convenience, they relied on lists they had available, rather than try to ensure every American was included on their list. A representative poll of 2 million people will give you more accurate results than a representative poll of 2 thousand, but I’ll take the smaller more representative poll than a larger one that uses convenience sampling any day.

7.5 Summary

Picking the right type of sample is critical to getting an accurate answer to your reserach question. There are a lot of differnet options in how you can select the people to participate in your research, but typically only one that is both correct and possible depending on the research you’re doing. In the next chapter we’ll talk about a few other methods for conducting reseach, some that don’t include any sampling by you.

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• Population vs Sample | Definitions, Differences & Examples

Population vs Sample | Definitions, Differences & Examples

Published on 3 May 2022 by Pritha Bhandari . Revised on 5 December 2022.

A population is the entire group that you want to draw conclusions about.

A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.

In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organisations, countries, species, or organisms.

Table of contents

Collecting data from a population, collecting data from a sample, population parameter vs sample statistic, practice questions: populations vs samples, frequently asked questions about samples and populations.

Populations are used when your research question requires, or when you have access to, data from every member of the population.

Usually, it is only straightforward to collect data from a whole population when it is small, accessible and cooperative.

For larger and more dispersed populations, it is often difficult or impossible to collect data from every individual. For example, every 10 years, the federal US government aims to count every person living in the country using the US Census. This data is used to distribute funding across the nation.

However, historically, marginalised and low-income groups have been difficult to contact, locate, and encourage participation from. Because of non-responses, the population count is incomplete and biased towards some groups, which results in disproportionate funding across the country.

In cases like this, sampling can be used to make more precise inferences about the population.

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When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sample. With statistical analysis , you can use sample data to make estimates or test hypotheses about population data.

Ideally, a sample should be randomly selected and representative of the population. Using probability sampling methods (such as simple random sampling or stratified sampling ) reduces the risk of sampling bias and enhances both internal and external validity .

For practical reasons, researchers often use non-probability sampling methods . Non-probability samples are chosen for specific criteria; they may be more convenient or cheaper to access. Because of non-random selection methods, any statistical inferences about the broader population will be weaker than with a probability sample.

Reasons for sampling

• Necessity : Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility.
• Practicality : It’s easier and more efficient to collect data from a sample.
• Cost-effectiveness : There are fewer participant, laboratory, equipment, and researcher costs involved.
• Manageability : Storing and running statistical analyses on smaller datasets is easier and reliable.

When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data. A parameter is a measure that describes the whole population. A statistic is a measure that describes the sample.

You can use estimation or hypothesis testing to estimate how likely it is that a sample statistic differs from the population parameter.

Sampling error

A sampling error is the difference between a population parameter and a sample statistic. In your study, the sampling error is the difference between the mean political attitude rating of your sample and the true mean political attitude rating of all undergraduate students in the Netherlands.

Sampling errors happen even when you use a randomly selected sample. This is because random samples are not identical to the population in terms of numerical measures like means and standard deviations .

Because the aim of scientific research is to generalise findings from the sample to the population, you want the sampling error to be low. You can reduce sampling error by increasing the sample size.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

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• Population and Samples

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Population and samples: the complete guide.

9 min read What are the differences between populations and samples? In this guide, we’ll discuss the two, as well as how to use them effectively in your research.

When we hear the term population, the first thing that comes to mind is a large group of people.

In market research, however, a population is an entire group that you want to draw conclusions about and possesses a standard parameter that is consistent throughout the group.

It’s important to note that a population doesn’t always refer to people, it can mean anything you want to study: objects, organizations, animals, chemicals and so on.

For example, all the countries in the world are an example of a population — or even the number of males in the UK. The size of the population can vary according to the target entities in question and the scope of the research.

When do you need to collect data from a population?

You use populations when your research calls for or requires you to collect data from every member of the population. Note: it’s normally far easier to collect data from whole populations when they’re small and accessible.

For larger and more diverse populations, on the other hand — e.g. a regional study on people living in Europe — while you would get findings representative of the entire population (as they’re all included in the study), it would take a considerable amount of time.

It’s in these instances that you use sampling. It allows you to make more precise inferences about the population as a whole, and streamline your research project. They’re typically used when population sizes are too large to include all possible members or inferences.

Let’s talk about samples.

What is a sample?

In statistical methods, a sample consists of a smaller group of entities, which are taken from the entire population. This creates a subset group that is easier to manage and has the characteristics of the larger population.

This smaller subset is then surveyed to gain information and data. The sample should reflect the population as a whole, without any bias towards a specific attribute or characteristic. In this way, researchers can ensure their results are representative and statistically significant.

To remove unconscious selection bias, a researcher may choose to randomize the selection of the sample.

Types of samples

There are two categories of sampling generally used – probability sampling and non-probability sampling :

• Probability sampling , also known as random sampling, is a kind of sample selection where randomization is used instead of deliberate choice.
• Non-probability sampling techniques involve the researcher deliberately picking items or individuals for the sample based on their research goals or knowledge

These two sampling techniques have several methods:

Probability sampling types include:

• Simple random sampling Every element in the population has an equal chance of being selected as part of the sample. Find out more about simple random sampling.
• Systematic sampling Also known as systematic clustering, in this method, random selection only applies to the first item chosen. A rule then applies so that every nth item or person after that is picked. Find out more about systematic sampling .
• Stratified random sampling Sampling uses random selection within predefined groups. Find out more about stratified random sampling .
• Cluster sampling Groups rather than individual units of the target population are selected at random.

Non-probability sampling types include:

• Convenience sampling People or elements in a sample are selected based on their availability.
• Quota sampling The sample is formed according to certain groups or criteria.
• Purposive sampling Also known as judgmental sampling. The sample is formed by the researcher consciously choosing entities, based on the survey goals.
• Snowball sampling Also known as referral sampling. The sample is formed by sample participants recruiting connections.

Find out more about sampling methods with our ultimate guide to sampling methods and best practices

Calculating sample size

Worried about sample sizes? You can also use our sample size calculator to determine how many responses you need to be confident in your data.

Go to sample size calculator

When to use sampling

As mentioned, sampling is useful for dealing with population data that is too large to process as a whole or is inaccessible. Sampling also helps to keep costs down and reduce time to insight.

Advantages of using sampling to collect data

• Provide researchers with a representative view of the population through the sample subset.
• The researcher has flexibility and control over what kind of sample they want to make, depending on their needs and the goals of the research.
• Reduces the volume of data, helping to save time.
• With proper methods, researchers can achieve a higher level of accuracy
• Researchers can get detailed information on a population with a smaller amount of resource
• Significantly cheaper than other methods
• Allows for deeper study of some aspects of data — rather than asking 15 questions to every individual, it’s better to use 50 questions on a representative sample

Disadvantages of using sampling to collect data

• Researcher bias can affect the quality and accuracy of results
• Sampling studies require well-trained experts
• Even with good survey design, there’s no way to eliminate sampling errors entirely
• People in the sample may refuse to respond
• Probability sampling methods can be less representative in favor of random allocation.
• Improper selection of sampling techniques can affect the entire process negatively

How can you use sampling in business?

Depending on the nature of your study and the conclusions you wish to draw, you’ll have to select an appropriate sampling method as mentioned above. That said, here are a few examples of how you can use sampling techniques in business.

Creating a new product

If you’re looking to create a new product line, you may want to do panel interviews or surveys with a representative sample for the new market. By showing your product or concept to a sample that represents your target audience (population), you ensure that the feedback you receive is more reflective of how that customer segment will feel.

Average employee performance

If you wanted to understand the average employee performance for a specific group, you could use a random sample from a team or department (population). As every person in the department has a chance of being selected, you’ll have a truly random — yet representative sample. From the data collected, you can make inferences about the team/department’s average performance.

Store feedback

Let’s say you want to collect feedback from customers who are shopping or have just finished shopping at your store. To do this, you could use convenience sampling. It’s fast, affordable and done at a point of convenience. You can use this to get a quick gauge of how people feel about your store’s shopping experience — but it won’t represent the true views of all your customers.

Manage your population and sample data easily

Whatever the sample size of your target audience, there are several things to consider:

• How can you save time in conducting the research?
• How do you analyze and compare all the responses?
• How can you track and chase non-respondents easily?
• How can you translate the data into a usable presentation format?
• How can you share this easily?

These questions can make the task of supporting internal teams and management difficult.

This is where the Qualtrics CoreXM technology solution can help you progress through research with ease.

It includes:

• Advanced AI and machine learning tools to easily analyze data from open-text responses and data, giving you actionable insights at scale.
• Intuitive drag-and-drop survey building with powerful logic, 100+ question types, and pre-built survey templates . For more information on how to get started on your survey creation, visit our complete guide on creating a survey.
• Stylish, accessible and easy-to-understand reporting that automatically updates in real time, so everyone in your organization has the latest insights at their fingertips.
• Powerful automation to get up and running quickly with out-of-the-box workflows, including guided setup and proactive recommendations to help you connect with other teams and react fast to changes.

Also, the Qualtrics online research panels and samples help you to:

• Choose a target audience and get access to a representative sample
• Boost the accuracy of your research with a sample methodology that’s 47% more consistent than standard sampling methods
• Get dedicated support at every stage, from launching your survey to reporting on the results.

Want to learn more?

Related resources

Panels & Samples

Representative Samples 13 min read

Reward survey participants 15 min read, panel management 14 min read, what is a research panel 10 min read.

Analysis & Reporting

Data Saturation In Qualitative Research 8 min read

How to determine sample size 12 min read.

Market Segmentation

User Personas 14 min read

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Data Collection Methods

Population vs sample in research: What’s the difference?

Population and sample are two important terms in research. Having a thorough understanding of these terms is important if you want to conduct effective research — and that’s especially true for new researchers. If you need a primer on population vs sample, this article covers everything you need to know, including how to collect data from either group.

What is a population?

Outside the research field, population refers to the number of people living in a place at a particular time. In research, however, a population is a well-defined group of people or items that share the same characteristics. It’s the group that a researcher is interested in studying.

Arvind Sharma , an assistant professor at Boston College, explains that a population isn’t limited to people: “It can be any unit from which you obtain data to carry out your research.” This group could consist of humans, animals, or objects.

Below are some examples of population:

• Male adults in the United States
• World Cup football matches
• Insects in American rainforests

As you can see from the examples above, populations are usually large, so it’s often difficult to survey an entire population. That’s where sampling comes in.

What is a sample?

A sample is a select group of individuals from the research population. A sample is only a subset or a subgroup of the population and, by definition, is always smaller than the population. However, well-selected samples accurately represent the entire population.

Below are some examples to illustrate the differences between population vs sample:

The sample a researcher choses from any population will depend on their research goals and objectives. For example, if you’re researching employees in a large corporation, you may be interested in C-level executives, junior-level employees, or even external contractors.

What are the differences between population and sample?

Below are the main differences between a population and a sample, as pointed out by Sharma:

What are some reasons for sampling?

Collecting data from an entire population isn’t always possible. “In fact,” explains Sharma, “99 percent of the time, we can’t survey the entire population. Other times, it is not even necessary.

“A representative sample drawn using appropriate sampling techniques will provide results that are representative of the entire population. So, it would be unnecessary to survey every member of the population.”

Below are the other most important reasons for using sampling.

Population studies are more expensive than sample surveys. For example, researching the entire population of adult male Americans would be too costly. It’s more cost-effective to work with a representative sample.

2. Practicality

Consider the adult male American research example. Even if a researcher had the resources to survey all the males in that population, it may be difficult or impossible to obtain responses from all participants. For example, the researchers may not even be able to contact all members of this population.

3. Manageability

It’s easier to manage time, costs, and resources when working with samples. Also, it’s easier to manage the data you collect from a sample vs a population. For example, it’s easier to analyze data from a sample of 1,000 adult males than a sample of all adult males in the U.S. or even a specific state.

How can you collect data from a population?

Collecting data from an entire population requires a census. A census is a collection of information from all sections of the population. It’s a complete enumeration of the population, and it requires considerable resources, which is why researchers often work with a sample.

If the target population is small, however, then you can collect data from every member of the population. For example, you can survey the performance of the members of the customer service team in a bank branch. The number is likely to be more manageable, so you can access and collect data from this population.

What methods can you use to collect data from a sample?

There are so many approaches for collecting data from samples. Some of the more commonly used methods are listed below.

1. Simple random sampling

In simple random sampling, researchers select individuals at random from the population. In this method, every member of the population has an equal chance of being selected.

For example, suppose you want to select a sample of 50 employees from a population of 500 employees. You could write down all the names of the employees, place them in a hat or container, and pick employee names at random like you would in a lottery. That’s an example of simple random sampling. It works best when the population isn’t too large.

2. Systematic sampling

This is a sampling technique that selects every k th item from the population. It’s a type of probability sampling researchers use to select items from a population randomly. A researcher may want to use this technique if they’re working with a large population and need to sample only a small number of items in order to study them in detail.

For example, to apply systematic sampling in a performance survey of 1,000 customer service team members, we can choose every fifth member — i.e., the fifth, 10th, 15th customer service rep, and so on.

For more details on  what is systematic sampling , check out our guide

3. Stratified sampling

In this probability sampling method, researchers divide members of the population into groups based on age, race, ethnicity, or sex. Researchers select individuals randomly from those groups to form a sample. This ensures that every group is equally represented.

What is a sampling error?

A sampling error is the difference between the value obtained from a sample and the true population value. It’s the difference between an estimate from a sample and the true population value.

A sampling error can occur if you don’t have enough people in your sample or if you select people who aren’t representative of the population. This can impact the accuracy of your survey. For example, if you want to know what percentage of adults are vegetarian but only ask vegetarians in a specific city, then this would be an example of selecting people who aren’t representative of the population.

According to Sharma, you can reduce sampling errors by increasing the sample size . He also notes that sample design and variation within a population affect sampling errors.

How can Jotform make the research process easier?

Whether you’re surveying a small or large sample or even an entire population, Jotform gives you the right tools to make your research easier. With Jotform’s free online survey maker, you can create engaging surveys and collect responses online. You can easily customize any of our 10,000-plus free survey templates to suit your research purposes. Get started with Jotform today.

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Sampling Methods In Reseach: Types, Techniques, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.

• Sampling : the process of selecting a representative group from the population under study.
• Target population : the total group of individuals from which the sample might be drawn.
• Sample: a subset of individuals selected from a larger population for study or investigation. Those included in the sample are termed “participants.”
• Generalizability : the ability to apply research findings from a sample to the broader target population, contingent on the sample being representative of that population.

For instance, if the advert for volunteers is published in the New York Times, this limits how much the study’s findings can be generalized to the whole population, because NYT readers may not represent the entire population in certain respects (e.g., politically, socio-economically).

The Purpose of Sampling

We are interested in learning about large groups of people with something in common in psychological research. We call the group interested in studying our “target population.”

In some types of research, the target population might be as broad as all humans. Still, in other types of research, the target population might be a smaller group, such as teenagers, preschool children, or people who misuse drugs.

Studying every person in a target population is more or less impossible. Hence, psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.

This is important because we want to generalize from the sample to the target population. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population.

One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population.

Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants (e.g., Asch ).

OK, so you’ve thought up this brilliant psychological study and designed it perfectly. But who will you try it out on, and how will you select your participants?

There are various sampling methods. The one chosen will depend on a number of factors (such as time, money, etc.).

Random Sampling

Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.

This is similar to the national lottery. If the “population” is everyone who bought a lottery ticket, then everyone has an equal chance of winning the lottery (assuming they all have one ticket each).

Random samples require naming or numbering the target population and then using some raffle method to choose those to make up the sample. Random samples are the best method of selecting your sample from the population of interest.

• The advantages are that your sample should represent the target population and eliminate sampling bias.
• The disadvantage is that it is very difficult to achieve (i.e., time, effort, and money).

Stratified Sampling

During stratified sampling , the researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable (e.g., IQ, gender, etc.) that might have an effect on the research. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.

For example, students studying English Literature may spend more money on books than engineering students, so if we use a large percentage of English students or engineering students, our results will not be accurate.

We have to determine the relative percentage of each group at a university, e.g., Engineering 10%, Social Sciences 15%, English 20%, Sciences 25%, Languages 10%, Law 5%, and Medicine 15%. The sample must then contain all these groups in the same proportion as the target population (university students).

• The disadvantage of stratified sampling is that gathering such a sample would be extremely time-consuming and difficult to do. This method is rarely used in Psychology.
• However, the advantage is that the sample should be highly representative of the target population, and therefore we can generalize from the results obtained.

Opportunity Sampling

Opportunity sampling is a method in which participants are chosen based on their ease of availability and proximity to the researcher, rather than using random or systematic criteria. It’s a type of convenience sampling .

An opportunity sample is obtained by asking members of the population of interest if they would participate in your research. An example would be selecting a sample of students from those coming out of the library.

• This is a quick and easy way of choosing participants (advantage)
• It may not provide a representative sample and could be biased (disadvantage).

Systematic Sampling

Systematic sampling is a method where every nth individual is selected from a list or sequence to form a sample, ensuring even and regular intervals between chosen subjects.

Participants are systematically selected (i.e., orderly/logical) from the target population, like every nth participant on a list of names.

To take a systematic sample, you list all the population members and then decide upon a sample you would like. By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call n.

If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample 150 children from a school of 1,500, you would take every 10th name.

• The advantage of this method is that it should provide a representative sample.

Sample size

The sample size is a critical factor in determining the reliability and validity of a study’s findings. While increasing the sample size can enhance the generalizability of results, it’s also essential to balance practical considerations, such as resource constraints and diminishing returns from ever-larger samples.

Reliability and Validity

Reliability refers to the consistency and reproducibility of research findings across different occasions, researchers, or instruments. A small sample size may lead to inconsistent results due to increased susceptibility to random error or the influence of outliers. In contrast, a larger sample minimizes these errors, promoting more reliable results.

Validity pertains to the accuracy and truthfulness of research findings. For a study to be valid, it should accurately measure what it intends to do. A small, unrepresentative sample can compromise external validity, meaning the results don’t generalize well to the larger population. A larger sample captures more variability, ensuring that specific subgroups or anomalies don’t overly influence results.

Practical Considerations

Resource Constraints : Larger samples demand more time, money, and resources. Data collection becomes more extensive, data analysis more complex, and logistics more challenging.

Diminishing Returns : While increasing the sample size generally leads to improved accuracy and precision, there’s a point where adding more participants yields only marginal benefits. For instance, going from 50 to 500 participants might significantly boost a study’s robustness, but jumping from 10,000 to 10,500 might not offer a comparable advantage, especially considering the added costs.

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Home » Sampling Methods – Types, Techniques and Examples

Sampling Methods – Types, Techniques and Examples

Table of Contents

Sampling refers to the process of selecting a subset of data from a larger population or dataset in order to analyze or make inferences about the whole population.

In other words, sampling involves taking a representative sample of data from a larger group or dataset in order to gain insights or draw conclusions about the entire group.

Sampling Methods

Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research.

Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of that population, which can be time-consuming, expensive, or even impossible.

Types of Sampling Methods

Sampling can be broadly categorized into two main categories:

Probability Sampling

This type of sampling is based on the principles of random selection, and it involves selecting samples in a way that every member of the population has an equal chance of being included in the sample.. Probability sampling is commonly used in scientific research and statistical analysis, as it provides a representative sample that can be generalized to the larger population.

Type of Probability Sampling :

• Simple Random Sampling: In this method, every member of the population has an equal chance of being selected for the sample. This can be done using a random number generator or by drawing names out of a hat, for example.
• Systematic Sampling: In this method, the population is first divided into a list or sequence, and then every nth member is selected for the sample. For example, if every 10th person is selected from a list of 100 people, the sample would include 10 people.
• Stratified Sampling: In this method, the population is divided into subgroups or strata based on certain characteristics, and then a random sample is taken from each stratum. This is often used to ensure that the sample is representative of the population as a whole.
• Cluster Sampling: In this method, the population is divided into clusters or groups, and then a random sample of clusters is selected. Then, all members of the selected clusters are included in the sample.
• Multi-Stage Sampling : This method combines two or more sampling techniques. For example, a researcher may use stratified sampling to select clusters, and then use simple random sampling to select members within each cluster.

Non-probability Sampling

This type of sampling does not rely on random selection, and it involves selecting samples in a way that does not give every member of the population an equal chance of being included in the sample. Non-probability sampling is often used in qualitative research, where the aim is not to generalize findings to a larger population, but to gain an in-depth understanding of a particular phenomenon or group. Non-probability sampling methods can be quicker and more cost-effective than probability sampling methods, but they may also be subject to bias and may not be representative of the larger population.

Types of Non-probability Sampling :

• Convenience Sampling: In this method, participants are chosen based on their availability or willingness to participate. This method is easy and convenient but may not be representative of the population.
• Purposive Sampling: In this method, participants are selected based on specific criteria, such as their expertise or knowledge on a particular topic. This method is often used in qualitative research, but may not be representative of the population.
• Snowball Sampling: In this method, participants are recruited through referrals from other participants. This method is often used when the population is hard to reach, but may not be representative of the population.
• Quota Sampling: In this method, a predetermined number of participants are selected based on specific criteria, such as age or gender. This method is often used in market research, but may not be representative of the population.
• Volunteer Sampling: In this method, participants volunteer to participate in the study. This method is often used in research where participants are motivated by personal interest or altruism, but may not be representative of the population.

Applications of Sampling Methods

Applications of Sampling Methods from different fields:

• Psychology : Sampling methods are used in psychology research to study various aspects of human behavior and mental processes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and ethnicity. Random sampling may also be used to select participants for experimental studies.
• Sociology : Sampling methods are commonly used in sociological research to study social phenomena and relationships between individuals and groups. For example, researchers may use cluster sampling to select a sample of neighborhoods to study the effects of economic inequality on health outcomes. Stratified sampling may also be used to select a sample of participants that is representative of the population based on factors such as income, education, and occupation.
• Social sciences: Sampling methods are commonly used in social sciences to study human behavior and attitudes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and income.
• Marketing : Sampling methods are used in marketing research to collect data on consumer preferences, behavior, and attitudes. For example, researchers may use random sampling to select a sample of consumers to participate in a survey about a new product.
• Healthcare : Sampling methods are used in healthcare research to study the prevalence of diseases and risk factors, and to evaluate interventions. For example, researchers may use cluster sampling to select a sample of health clinics to participate in a study of the effectiveness of a new treatment.
• Environmental science: Sampling methods are used in environmental science to collect data on environmental variables such as water quality, air pollution, and soil composition. For example, researchers may use systematic sampling to collect soil samples at regular intervals across a field.
• Education : Sampling methods are used in education research to study student learning and achievement. For example, researchers may use stratified sampling to select a sample of schools that is representative of the population based on factors such as demographics and academic performance.

Examples of Sampling Methods

Probability Sampling Methods Examples:

• Simple random sampling Example : A researcher randomly selects participants from the population using a random number generator or drawing names from a hat.
• Stratified random sampling Example : A researcher divides the population into subgroups (strata) based on a characteristic of interest (e.g. age or income) and then randomly selects participants from each subgroup.
• Systematic sampling Example : A researcher selects participants at regular intervals from a list of the population.

Non-probability Sampling Methods Examples:

• Convenience sampling Example: A researcher selects participants who are conveniently available, such as students in a particular class or visitors to a shopping mall.
• Purposive sampling Example : A researcher selects participants who meet specific criteria, such as individuals who have been diagnosed with a particular medical condition.
• Snowball sampling Example : A researcher selects participants who are referred to them by other participants, such as friends or acquaintances.

How to Conduct Sampling Methods

some general steps to conduct sampling methods:

• Define the population: Identify the population of interest and clearly define its boundaries.
• Choose the sampling method: Select an appropriate sampling method based on the research question, characteristics of the population, and available resources.
• Determine the sample size: Determine the desired sample size based on statistical considerations such as margin of error, confidence level, or power analysis.
• Create a sampling frame: Develop a list of all individuals or elements in the population from which the sample will be drawn. The sampling frame should be comprehensive, accurate, and up-to-date.
• Select the sample: Use the chosen sampling method to select the sample from the sampling frame. The sample should be selected randomly, or if using a non-random method, every effort should be made to minimize bias and ensure that the sample is representative of the population.
• Collect data: Once the sample has been selected, collect data from each member of the sample using appropriate research methods (e.g., surveys, interviews, observations).
• Analyze the data: Analyze the data collected from the sample to draw conclusions about the population of interest.

When to use Sampling Methods

Sampling methods are used in research when it is not feasible or practical to study the entire population of interest. Sampling allows researchers to study a smaller group of individuals, known as a sample, and use the findings from the sample to make inferences about the larger population.

Sampling methods are particularly useful when:

• The population of interest is too large to study in its entirety.
• The cost and time required to study the entire population are prohibitive.
• The population is geographically dispersed or difficult to access.
• The research question requires specialized or hard-to-find individuals.
• The data collected is quantitative and statistical analyses are used to draw conclusions.

Purpose of Sampling Methods

The main purpose of sampling methods in research is to obtain a representative sample of individuals or elements from a larger population of interest, in order to make inferences about the population as a whole. By studying a smaller group of individuals, known as a sample, researchers can gather information about the population that would be difficult or impossible to obtain from studying the entire population.

Sampling methods allow researchers to:

• Study a smaller, more manageable group of individuals, which is typically less time-consuming and less expensive than studying the entire population.
• Reduce the potential for data collection errors and improve the accuracy of the results by minimizing sampling bias.
• Make inferences about the larger population with a certain degree of confidence, using statistical analyses of the data collected from the sample.
• Improve the generalizability and external validity of the findings by ensuring that the sample is representative of the population of interest.

Characteristics of Sampling Methods

Here are some characteristics of sampling methods:

• Randomness : Probability sampling methods are based on random selection, meaning that every member of the population has an equal chance of being selected. This helps to minimize bias and ensure that the sample is representative of the population.
• Representativeness : The goal of sampling is to obtain a sample that is representative of the larger population of interest. This means that the sample should reflect the characteristics of the population in terms of key demographic, behavioral, or other relevant variables.
• Size : The size of the sample should be large enough to provide sufficient statistical power for the research question at hand. The sample size should also be appropriate for the chosen sampling method and the level of precision desired.
• Efficiency : Sampling methods should be efficient in terms of time, cost, and resources required. The method chosen should be feasible given the available resources and time constraints.
• Bias : Sampling methods should aim to minimize bias and ensure that the sample is representative of the population of interest. Bias can be introduced through non-random selection or non-response, and can affect the validity and generalizability of the findings.
• Precision : Sampling methods should be precise in terms of providing estimates of the population parameters of interest. Precision is influenced by sample size, sampling method, and level of variability in the population.
• Validity : The validity of the sampling method is important for ensuring that the results obtained from the sample are accurate and can be generalized to the population of interest. Validity can be affected by sampling method, sample size, and the representativeness of the sample.

Advantages of Sampling Methods

Sampling methods have several advantages, including:

• Cost-Effective : Sampling methods are often much cheaper and less time-consuming than studying an entire population. By studying only a small subset of the population, researchers can gather valuable data without incurring the costs associated with studying the entire population.
• Convenience : Sampling methods are often more convenient than studying an entire population. For example, if a researcher wants to study the eating habits of people in a city, it would be very difficult and time-consuming to study every single person in the city. By using sampling methods, the researcher can obtain data from a smaller subset of people, making the study more feasible.
• Accuracy: When done correctly, sampling methods can be very accurate. By using appropriate sampling techniques, researchers can obtain a sample that is representative of the entire population. This allows them to make accurate generalizations about the population as a whole based on the data collected from the sample.
• Time-Saving: Sampling methods can save a lot of time compared to studying the entire population. By studying a smaller sample, researchers can collect data much more quickly than they could if they studied every single person in the population.
• Less Bias : Sampling methods can reduce bias in a study. If a researcher were to study the entire population, it would be very difficult to eliminate all sources of bias. However, by using appropriate sampling techniques, researchers can reduce bias and obtain a sample that is more representative of the entire population.

Limitations of Sampling Methods

• Sampling Error : Sampling error is the difference between the sample statistic and the population parameter. It is the result of selecting a sample rather than the entire population. The larger the sample, the lower the sampling error. However, no matter how large the sample size, there will always be some degree of sampling error.
• Selection Bias: Selection bias occurs when the sample is not representative of the population. This can happen if the sample is not selected randomly or if some groups are underrepresented in the sample. Selection bias can lead to inaccurate conclusions about the population.
• Non-response Bias : Non-response bias occurs when some members of the sample do not respond to the survey or study. This can result in a biased sample if the non-respondents differ from the respondents in important ways.
• Time and Cost : While sampling can be cost-effective, it can still be expensive and time-consuming to select a sample that is representative of the population. Depending on the sampling method used, it may take a long time to obtain a sample that is large enough and representative enough to be useful.
• Limited Information : Sampling can only provide information about the variables that are measured. It may not provide information about other variables that are relevant to the research question but were not measured.
• Generalization : The extent to which the findings from a sample can be generalized to the population depends on the representativeness of the sample. If the sample is not representative of the population, it may not be possible to generalize the findings to the population as a whole.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The American Trends Panel

What is the american trends panel (atp).

The ATP is Pew Research Center’s nationally representative online survey panel. The panel is composed of more than 10,000 adults selected at random from across the entire U.S.

Respondents have been recruited over the years, and they take our surveys frequently.   The panel provides a relatively efficient method of data collection compared with fresh samples because the participants have already agreed to take part in more surveys. The major effort required with a fresh sample – making an initial contact, persuading respondents to take part and gathering the necessary demographic information for weighting – is not needed once a respondent has joined a panel. Another advantage of the ATP is that considerable information about panelists’ views and experiences can be accumulated over time. Because panelists may respond to multiple surveys on different topics, it is possible to build a much richer portrait of the public than is feasible in a single survey interview, which must be limited in length to prevent respondent fatigue.

But panels like the ATP have some limitations as well. They can be expensive to create and maintain, requiring more extensive technical skill and oversight than a one-off survey. A second concern is that panelists may drop out over time (known as attrition), making the panel less representative of the target population as time passes if the kinds of people who drop out are different from those who tend to remain. The ATP features an annual recruitment for new panelists from across the country to address this.

Another concern is that repeated questioning of the same individuals may yield different results than we would obtain with independent or “fresh” samples. If the same questions are asked repeatedly, respondents may remember their answers and feel some pressure to be consistent over time. The reverse is also a concern, as respondents might become “conditioned” to change their behavior because of questions asked previously. For example, questions about voting might spur them to register to vote. Respondents also become more skilled at answering particular kinds of questions. This may be beneficial in some instances, but to the extent it occurs, the panel results may be different from what would have been obtained from independent samples of people who have not had the practice in responding to surveys. Fortunately, research has detected no meaningful conditioning on the ATP .

Recruiting panelists to the ATP

The ATP was created in 2014, with the first cohort of panelists invited to join the panel at the end of a large, national, landline and cellphone random-digit-dial survey that was conducted in both English and Spanish. Two additional recruitments were conducted using the same method in 2015 and 2017, respectively.

In 2018, the ATP switched from telephone to address-based recruitment. Invitations are sent to a random, address-based sample (ABS) of households selected from the U.S. Postal Service’s Delivery Sequence File (DSF).

Randomization in sampling is carried through right down to the household level to maintain representativeness of sample. The adult with the next birthday in each selected household is asked to go online to complete a survey, at the end of which they are invited to join the panel.

For the online panel to be truly nationally representative, the share of those who do not use the internet must be represented on the panel somehow. In 2021, the share of non-internet users in the U.S was  estimated  to be 7%, and while this is a relatively small group, its members are quite different demographically from those who go online. In its early years, the ATP conducted interviews with non-internet users via paper questionnaires. However, in 2016, the Center switched to providing non-internet households with tablets which they could use to take the surveys online. The Center works with Ipsos , an international market and opinion research organization, to recruit panelists, manage the panel and conduct the surveys.  

Drawing samples for ATP surveys

One of the benefits of a large panel like the ATP is that there are more panelists than a typical survey requires. Rather than selecting all 10,000-plus panelists each time, many ATP surveys interview only a subset (e.g., 2,500) of the panelists. This reduces the burden on individual panel members, sparing them from having to respond every time the Center fields a survey.

Drawing subsamples (rather than interviewing everyone on the panel) also allows Center researchers to make the samples more representative. Like most survey panels, the ATP has proportionately too many of some groups (e.g., college-educated individuals) and proportionately too few of others (e.g., young adults). ATP subsamples address these imbalances so that the responding sample looks quite like the U.S. public overall. This produces a sample that requires less weighting to align it with the population and, thus, a larger effective sample size.

Fielding ATP surveys

It takes about seven days for a questionnaire to be translated, programmed and made ready for its first phase of testing. The main objective during testing is to make sure that questions render on screen the way they ought to, and that question logic and skip patterns are all in place. For example, if the response to “How many people live in your household, including you?” is “1,” subsequent questions asking about other household members, such as “Are you the parent or guardian of any children under the age of 12 who live in your household?” should be skipped. Questions that require a number entry are also checked to make sure that incorrect formats (for example, decimals) are not an option when asking questions such as “What year were you born?” Lastly, this stage of testing is when most formatting changes and typos are caught and flagged for correction. Certain questions are programmed differently for mobile devices so that questions and text appear legibly without the need for horizontal, and sometimes (excessive) vertical scrolling.

A day or two are dedicated to fixing any programming errors that may have been detected during testing, as well as to updating the draft questionnaire and graduating it to a final stage. Programmers then send final test links (called “end to end” links) for final checking before survey launch. This round of testing verifies that the survey is working on all of the most common web browsers (e.g., Edge, Chrome, Mozilla, Safari) and devices (e.g., personal computers, Android phones, iPads, iPhones). Researchers also conduct breakoff tests to ensure that respondents can continue their survey at a later stage and on a different device.

Once testing is completed, the survey is ready for fielding. This process begins with a “soft launch,” wherein about 60 reliably fast ATP panelists are notified that the survey is ready. The soft launch typically takes place a day before full launch of the survey. Data quality checks are performed on this initial dataset to check the data format and to allow sufficient time to course correct any issues in the program that may be flagged before the survey gets sent out to all selected panelists for that survey. Survey invitations are sent out on the day the survey launches via email and, for panelists who consent to receiving SMS messages, via text messages as well. Several days after launch, panelists are sent up to two email or text reminders if they do not respond to the survey. From time to time, interactive voice recording reminder calls are also made to tablet households that previously provided consent to receive these reminders.

Data collection usually closes six to 14 days after full launch, depending on the research needs. Researchers then conduct a series of data quality checks to flag any issues with respondent satisficing or how answers were recorded in the dataset. All respondents are offered a post-paid incentive for their participation. Respondents choose to receive the post-paid incentive in the form of a check or a gift code to Amazon.com. Incentive amounts range depending on whether the respondent belongs to a part of the population that is harder or easier to reach. Differential incentive amounts are designed to increase panel survey participation among groups that traditionally have low survey response propensities.

Weighting ATP surveys

The ATP data is weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. First, each panelist begins with a base weight that reflects their probability of selection for their initial recruitment survey (and the probability of being invited to participate in the panel in cases where only a subsample of respondents were invited). The base weights for panelists recruited in different years are scaled to be proportionate to the effective sample size for all active panelists in their cohort. To correct for nonresponse to the initial recruitment surveys and gradual panel attrition, the base weights for all active panelists are calibrated to align with the population benchmarks identified in the accompanying table to create a full-panel weight.

For ATP waves in which only a subsample of panelists are invited to participate, a wave-specific base weight is created by adjusting the full-panel weights for subsampled panelists to account for any differential probabilities of selection for the particular panel wave. For waves in which all active panelists are invited to participate, the wave-specific base weight is identical to the full-panel weight.

In the final weighting step, the wave-specific base weights for panelists who completed the survey are again calibrated to match population benchmarks. The Center calibrates ATP surveys to both demographic benchmarks (e.g., age, education, sex, race, ethnicity, geography) and non-demographic benchmarks (e.g., political party affiliation, religious affiliation, registered voter status, volunteerism). These weights are then trimmed (typically at about the 1st and 99th percentiles) to reduce the loss in precision stemming from variance in the weights. Sampling errors and test of statistical significance take into account the effect of weighting.

Maintaining the ATP

Pew Research Center works with Ipsos to recruit panelists, manage the panel and conduct the surveys. Every year, we aim to “refresh” the panel by adding new panelists. Panelists also take an annual profile survey where they are given the opportunity to update certain aspects of their profile, such as their income, number of children, etc. If a panelist does not participate in the annual profile survey, they get retired from the panel. Occasionally, we also retire members that are demographically overrepresented on the panel, such as those who are more educated. We do this to ensure representation on the panel that resembles the general population, and to avoid having to weight certain panelists too heavily. We also retire panelists that have been inactive for a period and those who get flagged as repeat offenders on surveys for behaviors such as high refusal rates (skipping 80% or more questions) on two or more recent surveys.  

We make a promise to our panelists to protect their identity. Several checks and balances are in place to make sure that Pew Research Center remains true to its word. Personal identifying information (PII) such as a panelist’s name or county of residence is maintained solely by the core panel administration team and is never made available to the general public. In some cases, additional steps such as data swapping – randomly swapping certain values among a small number of respondents with similar characteristics for sensitive questions – is also used to protect panelists’ information. 

Learn more here about the history of the American Trends Panel , including its creation, development, and growth. 

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