what does it mean to be hypothesis driven

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Hypothesis-driven approach: the definitive guide

Imagine you are walking in one of McKinsey’s offices.

Around you, there are a dozen of busy consultants.

The word “hypothesis” would be one of the words you would hear the most.

Along with “MECE” or “what’s the so-what?”.

This would also be true in any BCG, Bain & Company office or other major consulting firms.

Because strategy consultants are trained to use a hypothesis-driven approach to solve problems.

And as a candidate, you must demonstrate your capacity to be hypothesis-driven in your case interviews .

There is no turnaround:

If you want a consulting offer, you MUST know how to use a hypothesis-driven approach .

Like a consultant would be hypothesis-driven on a real project for a real client?

Hell, no! Big mistake!

Because like any (somehow) complex topics in life, the context matters.

What is correct in one context becomes incorrect if the context changes.

And this is exactly what’s happening with using a hypothesis-driven approach in case interviews.

This should be different from the hypothesis-driven approach used by consultant solving a problem for a real client .

And that’s why many candidates get it wrong (and fail their interviews).

They use a hypothesis-driven approach like they were already a consultant.

Thus, in this article, you’ll learn the correct definition of being hypothesis-driven in the context of case interviews .

Plus, you’ll learn how to use a hypothesis in your case interviews to “crack the case”, and more importantly get the well-deserved offer!

Ready? Let’s go. It will be super interesting!

The wrong hypothesis-driven approach in case interviews.

Let’s start with a definition:

Hypothesis-driven thinking is a problem-solving method whereby you start with the answer and work back to prove or disprove that answer through fact-finding.

Concretely, here is how consultants use a hypothesis-driven approach to solve their clients’ problems:

Form an initial hypothesis, which is what they think the answer to the problem is.
Craft a logic issue tree , by asking themselves “what needs to be true for the hypothesis to be true?”
Walk their way down the issue tree and gather the necessary data to validate (or refute) the hypothesis.
Reiterate the process from step 1 – if their first hypothesis was disproved by their analysis – until they get it right.

With this answer-first approach, consultants do not gather data to fish for an answer. They seek to test their hypotheses , which is a very efficient problem-solving process.

The answer-first thinking works well if the initial hypothesis has been carefully formed.

This is why – in top consulting firms like McKinsey , BCG , or Bain & Company – the hypothesis is formed by a Partner with 20+ years of work experience.

And this is why this is NOT the right approach for case interviews.

Imagine a candidate doing a case interview at McKinsey and using answer-first thinking.

At the beginning of a case, this candidate forms a hypothesis (a potential answer to the problem), builds a logic tree, and gathers data to prove the hypothesis.

Here, there are two options:

The initial hypothesis is right

The initial hypothesis is wrong

If the hypothesis is right, what does it mean for the candidate?

That the candidate was lucky.

Nothing else.

And it certainly does not prove the problem-solving skills of this candidate (which is what is tested in case interviews).

Now, if the hypothesis is wrong, what’s happening next?

The candidate reiterates the process.

Imagine how disorganized the discussion with the interviewer can be.

Most of the time, such candidates cannot form another hypothesis, the case stops, and the candidate feels miserable.

This leads us to the right hypothesis-driven approach for case interviews.

The right hypothesis-driven approach in case interviews

To make my point clear between the wrong and right approach, I’ll take a non-business example.

Let’s imagine you want to move from point A to point B.

And for that, you have the choice among a multitude of roads.

Using the answer-first approach presented in the last section, you’d know which road to take to move from A to B (for instance the red line in the drawing below).

Again, this would not demonstrate your capacity to find the “best” road to go from A to B.

(regardless of what “best” means. It can be the fastest or the safest for instance.)

Now, a correct hypothesis-driven approach consists in drawing a map with all the potential routes between A and B, and explaining at each intersection why you want to turn left or right (” my hypothesis is that we should turn right ”).

And in the context of case interviews?

In the above analogy:

A is the problem
B is the solution
All the potential routes are the issues in your issue tree

And the explanation of why you want to take a certain road instead of another would be your hypothesis.

Is the difference between the wrong and right hypothesis-driven approach clearer?

If not, don’t worry. You’ll find many more examples below in this article.

But, next, let’s address another important question.

Why you must (always) use a hypothesis in your case interviews

You must use a hypothesis in your case interviews for two reasons.

A hypothesis helps you focus on what’s important to solve the case

Using a hypothesis-driven approach is critical to solving a problem efficiently.

In other words:

A hypothesis will limit the number of analysis you need to perform to solve a problem.

Thus, this is a way to apply the 80/20 principle and prioritize the issues (from your MECE issue tree ) you want to investigate.

And this is very important because your time with your interviewer is limited (like is the time with your client on a real project).

Let’s take a simple example of a hypothesis:

The profits of your client have dropped.

And your initial analysis shows increasing costs and stagnating revenues.

So your hypothesis can be:

“I think something happened in our cost structure, causing the profit drop. Next, I’d like to understand better the cost structure of our clients and which cost items have changed recently.”

Here the candidate is rigorously “cutting” half of his/her issue tree (the revenue side) and will focus the case discussion on the cost side.

And this is a good example of a hypothesis in case interviews.

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A hypothesis tells your interviewers why you want to do an analysis

There is a road that you NEVER want to take.

On this road, the purpose of the questions asked by a candidate is not clear.

Here are a few examples:

“What’s the market size? growth?”

“Who are the main competitors? what are their market shares?”

“Have customer preferences changed in this market?”

This list of questions might be relevant to solve the problem at stake.

But how these questions help solve the problem is not addressed.

Or in other words, the logical connection between these questions and the problem needs to be included.

So, a better example would be:

“We discovered that our client’s sales have declined for the past three years. I would like to know if this is specific to our client or if the whole market has the same trend. Can you tell me how the market size has changed over the past three years? »

In the above question, the reason why the candidate wants to investigate the market is clear: to narrow down the analysis to an internal root cause or an external root cause.

Yet, I see only a few (great) candidates asking clear and purposeful questions.

You want to be one of these candidates.

How to use a hypothesis-driven approach in your case interviews?

At this stage, you understand the importance of a hypothesis-driven approach in case interviews:

You want to identify the most promising areas to analyze (remember that time is money ).

And there are two (and only two) ways to create a good hypothesis in your case interviews:

a quantitative way
a qualitative way

Let’s start with the quantitative way to develop a good hypothesis in your case interviews.

The quantitative approach: use the available data

Let’s use an example to understand this data-driven approach:

Interviewer: your client is manufacturing computers. They have been experiencing increasing costs and want to know how to address this issue.

Candidate: to begin with, I want to know the breakdown of their cost structure. Do you have information about the % breakdown of their costs?

Interviewer: their materials costs count for 30% and their manufacturing costs for 60%. The last 10% are SG&A costs.

Candidate: Given the importance of manufacturing costs, I’d like to analyze this part first. Do we know if manufacturing costs go up?

Interviewer: yes, manufacturing costs have increased by 20% over the past 2 years.

Candidate: interesting. Now, it would be interesting to understand why such an increase happened.

You can notice in this example how the candidate uses data to drive the case discussion and prioritize which analysis to perform.

The candidate made a (correct) hypothesis that the increasing costs were driven by the manufacturing costs (the biggest chunk of the cost structure).

Even if the hypothesis were incorrect, the candidate would have moved closer to the solution by eliminating an issue (manufacturing costs are not causing the overall cost increase).

That said, there is another way to develop a good hypothesis in your case interviews.

The qualitative approach: use your business acumen

Sometimes you don’t have data (yet) to make a good hypothesis.

Thus, you must use your business judgment and develop a hypothesis.

Again, let’s take an example to illustrate this approach.

Interviewer: your client manufactures computers and has been losing market shares to their direct competitors. They hired us to find the root cause of this problem.

Candidate: I think of many reasons explaining the drop in market shares. First, our client manufactures and sells not-competitive products. Secondly, we might price our products too high. Third, we need to use the right distribution channels. For instance, we might sell in brick-and-mortars stores when consumers buy their computers in e-stores like Amazon. Finally, I think of our marketing expenses. There may be too low or not used strategically.

Candidate: I see these products as commodities where consumers use price as the main buying decision criteria. That’s why I’d like to explore how our client prices their products. Do you have information about how our prices compare to competitors’?

Interviewer: this is a valid point. Here is the data you want to analyze.

Note how this candidate explains what she/he wants to analyze first (prices) and why (computers are commodities).

In this case interview, the hypothesis-driven approach looks like this:

This is a commodity industry —> consumers buying behavior is driven by pricing —> our client’s prices are too high.

Again, note how the candidate first listed the potential root causes for this situation and did not use an answer-first approach.

Want to learn more?

In this free training , I explain in detail how to use data or your business acumen to prioritize the issues to analyze and “crack the case.”

Also, you’ll learn what to do if you don’t have data or can’t use your business acumen.

Form a hypothesis in these two critical moments of your case interviews

After you’ve presented your initial structure.

The first moment to form a hypothesis in your case interview?

In the beginning, after you’ve presented your structure.

When you’ve presented your issue tree, mention which issue you want to analyze first.

Also, explain why you want to investigate this first issue.

Make clear how the outcome of the analysis of this issue will help you solve the problem.

After an analysis

The second moment to form a hypothesis in your case interview?

After you’ve derived an insight from data analysis.

This insight has proved (or disproved) your hypothesis.

Either way, after you have developed an insight, you must form a new hypothesis.

This can be the issue you want to analyze next.

Or what a solution to the problem is.

Hypothesis-driven approach in case interviews: a conclusion

Having spent about 10 years coaching candidates through the consulting recruitment process , one commonality of successful candidates is that they truly understand how to be hypothesis-driven and demonstrate efficient problem-solving.

Plus, per my experience in coaching candidates , not being able to use a hypothesis is the second cause of rejection in case interviews (the first being the lack of MECEness ).

This means you can’t afford NOT to master this concept in a case study.

So, sign up now for this free course to learn how to use a hypothesis-driven approach in your case interviews and land your dream consulting job.

More than 7,000 people have already signed up.

Don’t waste one more minute!

See you there.

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Wednesday, February 5, 2014

Do's and don'ts for being hypothesis-driven at mckinsey, what a hypothesis is and is not..., what does it mean to be hypothesis-driven, important things to do when being hypothesis-driven....

Be sensitive to the fact that most non-consultants are not comfortable working this way
Inform your hypothesis with all available information including situational context
Involve others - especially your team - in developing and revising your hypothesis
Test and improve your hypothesis constantly as new information becomes available

Important things to NOT DO when being hypothesis-driven...

Wait until you have every fact you are seeking - you might be waiting forever
Waste time trying to make your hypothesis and supporting analyses perfect
Forget that your hypothesis should be a living document that is continuously improved...
Fail to question your hypothesis
Defend your hypothesis blindly
Adjust data to fit your hypothesis

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What is hypothesis-driven development?

Uncertainty is one of the biggest challenges of modern product development. Most often, there are more question marks than answers available.

This fact forces us to work in an environment of ambiguity and unpredictability.

Instead of combatting this, we should embrace the circumstances and use tools and solutions that excel in ambiguity. One of these tools is a hypothesis-driven approach to development.

Hypothesis-driven development in a nutshell

As the name suggests, hypothesis-driven development is an approach that focuses development efforts around, you guessed it, hypotheses.

To make this example more tangible, let’s compare it to two other common development approaches: feature-driven and outcome-driven.

In feature-driven development, we prioritize our work and effort based on specific features we planned and decided on upfront. The underlying goal here is predictability.

In outcome-driven development, the priorities are dictated not by specific features but by broader outcomes we want to achieve. This approach helps us maximize the value generated.

When it comes to hypothesis-driven development, the development effort is focused first and foremost on validating the most pressing hypotheses the team has. The goal is to maximize learning speed over all else.

Benefits of hypothesis-driven development

There are numerous benefits of a hypothesis-driven approach to development, but the main ones include:

Continuous learning

Mvp mindset, data-driven decision-making.

Hypothesis-driven development maximizes the amount of knowledge the team acquires with each release.

After all, if all you do is test hypotheses, each test must bring you some insight:

Continuous Learning With Hypothesis Driven Development Cycle Image

Hypothesis-driven development centers the whole prioritization and development process around learning.

Instead of designing specific features or focusing on big, multi-release outcomes, a hypothesis-driven approach forces you to focus on minimum viable solutions ( MVPs ).

After all, the primary thing you are aiming for is hypothesis validation. It often doesn’t require scalability, perfect user experience, and fully-fledged features.

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By definition, hypothesis-driven development forces you to truly focus on MVPs and avoid overcomplicating.

In hypothesis-driven development, each release focuses on testing a particular assumption. That test then brings you new data points, which help you formulate and prioritize next hypotheses.

That’s truly a data-driven development loop that leaves little room for HiPPOs (the highest-paid person in the room’s opinion).

Guide to hypothesis-driven development

Let’s take a look at what hypothesis-driven development looks like in practice. On a high level, it consists of four steps:

Formulate a list of hypotheses and assumptions
Prioritize the list
Design an MVP
Test and repeat

1. Formulate hypotheses

The first step is to list all hypotheses you are interested in.

Everything you wish to know about your users and market, as well as things you believe you know but don’t have tangible evidence to support, is a form of a hypothesis.

At this stage, I’m not a big fan of robust hypotheses such as, “We believe that if <we do something> then <something will happen> because <some user action>.”

To have such robust hypotheses, you need a solid enough understanding of your users, and if you do have it, then odds are you don’t need hypothesis-driven development anymore.

Instead, I prefer simpler statements that are closer to assumptions than hypotheses, such as:

“Our users will love the feature X”
“The option to do X is very important for student segment”
“Exam preparation is an important and underserved need that our users have”

2. Prioritize

The next step in hypothesis-driven development is to prioritize all assumptions and hypotheses you have. This will create your product backlog:

Prioritization Graphic With Cards In Order Of Descending Priority

There are various prioritization frameworks and approaches out there, so choose whichever you prefer. I personally prioritize assumptions based on two main criteria:

How much will we gain if we positively validate the hypothesis?
How much will we learn during the validation process?

Your priorities, however, might differ depending on your current context.

3. Design an MVP

Hypothesis-driven development is centered around the idea of MVPs — that is, the smallest possible releases that will help you gather enough information to validate whether a given hypothesis is true.

User experience, maintainability, and product excellence are secondary.

4. Test and repeat

The last step is to launch the MVP and validate whether the actual impact and consequent user behavior validate or invalidate the initial hypothesis.

The success isn’t measured by whether the hypothesis turned out to be accurate, but by how many new insights and learnings you captured during the process.

Based on the experiment, revisit your current list of assumptions, and, if needed, adjust the priority list.

Challenges of hypothesis-driven development

Although hypothesis-driven development comes with great benefits, it’s not all wine and roses.

Let’s take a look at a few core challenges that come with a hypothesis-focused approach.

Lack of robust product experience

Focusing on validating hypotheses and underlying MVP mindset comes at a cost. Robust product experience and great UX often require polishes, optimizations, and iterations, which go against speed-focused hypothesis-driven development.

You can’t optimize for both learning and quality simultaneously.

Unfocused direction

Although hypothesis-driven development is great for gathering initial learnings, eventually, you need to start developing a focused and sustainable long-term product strategy. That’s where outcome-driven development shines.

There’s an infinite amount of explorations you can do, but at some point, you must flip the switch and narrow down your focus around particular outcomes.

Over-emphasis on MVPs

Teams that embrace a hypothesis-driven approach often fall into the trap of an “MVP only” approach. However, shipping an actual prototype is not the only way to validate an assumption or hypothesis.

You can utilize tools such as user interviews, usability tests, market research, or willingness to pay (WTP) experiments to validate most of your doubts.

There’s a thin line between being MVP-focused in development and overusing MVPs as a validation tool.

When to use hypothesis-driven development

As you’ve most likely noticed, a hypothesis-driven development isn’t a multi-tool solution that can be used in every context.

On the contrary, its challenges make it an unsuitable development strategy for many companies.

As a rule of thumb, hypothesis-driven development works best in early-stage products with a high dose of ambiguity. Focusing on hypotheses helps bring enough clarity for the product team to understand where even to focus:

When To Use Hypothesis Driven Development Grid

But once you discover your product-market fit and have a solid idea for your long-term strategy, it’s often better to shift into more outcome-focused development. You should still optimize for learning, but it should no longer be the primary focus of your development effort.

While at it, you might also consider feature-driven development as a next step. However, that works only under particular circumstances where predictability is more important than the impact itself — for example, B2B companies delivering custom solutions for their clients or products focused on compliance.

Hypothesis-driven development can be a powerful learning-maximization tool. Its focus on MVP, continuous learning process, and inherent data-driven approach to decision-making are great tools for reducing uncertainty and discovering a path forward in ambiguous settings.

Honestly, the whole process doesn’t differ much from other development processes. The primary difference is that backlog and priories focus on hypotheses rather than features or outcomes.

Start by listing your assumptions, prioritizing them as you would any other backlog, and working your way top-to-bottom by shipping MVPs and adjusting priorities as you learn more about your market and users.

However, since hypothesis-driven development often lacks long-term cohesiveness, focus, and sustainable product experience, it’s rarely a good long-term approach to product development.

I tend to stick to outcome-driven and feature-driven approaches most of the time and resort to hypothesis-driven development if the ambiguity in a particular area is so hard that it becomes challenging to plan sensibly.

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HDD & More from Me

Hypothesis-Driven Development (Practitioner’s Guide)

Table of Contents

What is hypothesis-driven development (HDD)?

How do you know if it’s working, how do you apply hdd to ‘continuous design’, how do you apply hdd to application development, how do you apply hdd to continuous delivery, how does hdd relate to agile, design thinking, lean startup, etc..

Like agile, hypothesis-driven development (HDD) is more a point of view with various associated practices than it is a single, particular practice or process. That said, my goal here for is you to leave with a solid understanding of how to do HDD and a specific set of steps that work for you to get started.

After reading this guide and trying out the related practice you will be able to:

Diagnose when and where hypothesis-driven development (HDD) makes sense for your team
Apply techniques from HDD to your work in small, success-based batches across your product pipeline
Frame and enhance your existing practices (where applicable) with HDD

Does your product program feel like a Netflix show you’d binge watch? Is your team excited to see what happens when you release stuff? If so, congratulations- you’re already doing it and please hit me up on Twitter so we can talk about it! If not, don’t worry- that’s pretty normal, but HDD offers some awesome opportunities to work better.

Building on the scientific method, HDD is a take on how to integrate test-driven approaches across your product development activities- everything from creating a user persona to figuring out which integration tests to automate. Yeah- wow, right?! It is a great way to energize and focus your practice of agile and your work in general.

By product pipeline, I mean the set of processes you and your team undertake to go from a certain set of product priorities to released product. If you’re doing agile, then iteration (sprints) is a big part of making these work.

It wouldn’t be very hypothesis-driven if I didn’t have an answer to that! In the diagram above, you’ll find metrics for each area. For your application of HDD to what we’ll call continuous design, your metric to improve is the ratio of all your release content to the release content that meets or exceeds your target metrics on user behavior. For example, if you developed a new, additional way for users to search for products and set the success threshold at it being used in >10% of users sessions, did that feature succeed or fail by that measure? For application development, the metric you’re working to improve is basically velocity, meaning story points or, generally, release content per sprint. For continuous delivery, it’s how often you can release. Hypothesis testing is, of course, central to HDD and generally doing agile with any kind focus on valuable outcomes, and I think it shares the metric on successful release content with continuous design.

The first component is team cost, which you would sum up over whatever period you’re measuring. This includes ‘c $ ’, which is total compensation as well as loading (benefits, equipment, etc.) as well as ‘g’ which is the cost of the gear you use- that might be application infrastructure like AWS, GCP, etc. along with any other infrastructure you buy or share with other teams. For example, using a backend-as-a-service like Heroku or Firebase might push up your value for ‘g’ while deferring the cost of building your own app infrastructure.

The next component is release content, fe. If you’re already estimating story points somehow, you can use those. If you’re a NoEstimates crew, and, hey, I get it, then you’d need to do some kind of rough proportional sizing of your release content for the period in question. The next term, r f , is optional but this is an estimate of the time you’re having to invest in rework, bug fixes, manual testing, manual deployment, and anything else that doesn’t go as planned.

The last term, s d , is one of the most critical and is an estimate of the proportion of your release content that’s successful relative to the success metrics you set for it. For example, if you developed a new, additional way for users to search for products and set the success threshold at it being used in >10% of users sessions, did that feature succeed or fail by that measure? Naturally, if you’re not doing this it will require some work and changing your habits, but it’s hard to deliver value in agile if you don’t know what that means and define it against anything other than actual user behavior.

Here’s how some of the key terms lay out in the product pipeline:

The example here shows how a team might tabulate this for a given month:

Is the punchline that you should be shooting for a cost of $1,742 per story point? No. First, this is for a single month and would only serve the purpose of the team setting a baseline for itself. Like any agile practice, the interesting part of this is seeing how your value for ‘F’ changes from period to period, using your team retrospectives to talk about how to improve it. Second, this is just a single team and the economic value (ex: revenue) related to a given story point will vary enormously from product to product. There’s a Google Sheets-based calculator that you can use here: Innovation Accounting with ‘F’ .

Like any metric, ‘F’ only matters if you find it workable to get in the habit of measuring it and paying attention to it. As a team, say, evaluates its progress on OKR (objectives and key results), ‘F’ offers a view on the health of the team’s collaboration together in the context of their product and organization. For example, if the team’s accruing technical debt, that will show up as a steady increase in ‘F’. If a team’s invested in test or deploy automation or started testing their release content with users more specifically, that should show up as a steady lowering of ‘F’.

In the next few sections, we’ll step through how to apply HDD to your product pipeline by area, starting with continuous design.

It’s a mistake to ask your designer to explain every little thing they’re doing, but it’s also a mistake to decouple their work from your product’s economics. On the one hand, no one likes someone looking over their shoulder and you may not have the professional training to reasonably understand what they’re doing hour to hour, even day to day. On the other hand, it’s a mistake not to charter a designer’s work without a testable definition of success and not to collaborate around that.

Managing this is hard since most of us aren’t designers and because it takes a lot of work and attention to detail to work out what you really want to achieve with a given design.

Beginning with the End in Mind

The difference between art and design is intention- in design we always have one and, in practice, it should be testable. For this, I like the practice of customer experience (CX) mapping. CX mapping is a process for focusing the work of a team on outcomes–day to day, week to week, and quarter to quarter. It’s amenable to both qualitative and quantitative evidence but it is strictly focused on observed customer behaviors, as opposed to less direct, more lagging observations.

CX mapping works to define the CX in testable terms that are amenable to both qualitative and quantitative evidence. Specifically for each phase of a potential customer getting to behaviors that accrue to your product/market fit (customer funnel), it answers the following questions:

1. What do we mean by this phase of the customer funnel?

What do we mean by, say, ‘Acquisition’ for this product or individual feature? How would we know it if we see it?

2. How do we observe this (in quantitative terms)? What’s the DV?

This come next after we answer the question “What does this mean?”. The goal is to come up with a focal single metric (maybe two), a ‘dependent variable’ (DV) that tells you how a customer has behaved in a given phase of the CX (ex: Acquisition, Onboarding, etc.).

3. What is the cut off for a transition?

Not super exciting, but extremely important in actual practice, the idea here is to establish the cutoff for deciding whether a user has progressed from one phase to the next or abandoned/churned.

4. What is our ‘Line in the Sand’ threshold?

Popularized by the book ‘Lean Analytics’, the idea here is that good metrics are ones that change a team’s behavior (decisions) and for that you need to establish a threshold in advance for decision making.

5. How might we test this? What new IVs are worth testing?

The ‘independent variables’ (IV’s) you might test are basically just ideas for improving the DV (#2 above).

6. What’s tricky? What do we need to watch out for?

Getting this working will take some tuning, but it’s infinitely doable and there aren’t a lot of good substitutes for focusing on what’s a win and what’s a waste of time.

The image below shows a working CX map for a company (HVAC in a Hurry) that services commercial heating, ventilation, and air-conditioning systems. And this particular CX map is for the specific ‘job’/task/problem of how their field technicians get the replacement parts they need.

For more on CX mapping you can also check out it’s page- Tutorial: Customer Experience (CX) Mapping .

Unpacking Continuous Design for HDD

For the unpacking the work of design/Continuous Design with HDD , I like to use the ‘double diamond’ framing of ‘right problem’ vs. ‘right solution’, which I first learned about in Donald Norman’s seminal book, ‘The Design of Everyday Things’.

I’ve organized the balance of this section around three big questions:

How do you test that you’ve found the ‘Right Problem’?

How do you test that you’ve found demand and have the ‘right solution’, how do you test that you’ve designed the ‘right solution’.

Let’s say it’s an internal project- a ‘digital transformation’ for an HVAC (heating, ventilation, and air conditioning) service company. The digital team thinks it would be cool to organize the documentation for all the different HVAC equipment the company’s technicians service. But, would it be?

The only way to find out is to go out and talk to these technicians and find out! First, you need to test whether you’re talking to someone who is one of these technicians. For example, you might have a screening question like: ‘How many HVAC’s did you repair last week?’. If it’s <10, you might instead be talking to a handyman or a manager (or someone who’s not an HVAC tech at all).

Second, you need to ask non-leading questions. The evidentiary value of a specific answer to a general question is much higher than a specific answer to a specific questions. Also, some questions are just leading. For example, if you ask such a subject ‘Would you use a documentation system if we built it?’, they’re going to say yes, just to avoid the awkwardness and sales pitch they expect if they say no.

How do you draft personas? Much more renowned designers than myself (Donald Norman among them) disagree with me about this, but personally I like to draft my personas while I’m creating my interview guide and before I do my first set of interviews. Whether you draft or interview first is also of secondary important if you’re doing HDD- if you’re not iteratively interviewing and revising your material based on what you’ve found, it’s not going to be very functional anyway.

Really, the persona (and the jobs-to-be-done) is a means to an end- it should be answering some facet of the question ‘Who is our customer, and what’s important to them?’. It’s iterative, with a process that looks something like this:

How do you draft jobs-to-be-done? Personally- I like to work these in a similar fashion- draft, interview, revise, and then repeat, repeat, repeat.

You’ll use the same interview guide and subjects for these. The template is the same as the personas, but I maintain a separate (though related) tutorial for these–

A guide on creating Jobs-to-be-Done (JTBD) A template for drafting jobs-to-be-done (JTBD)

How do you interview subjects? And, action! The #1 place I see teams struggle is at the beginning and it’s with the paradox that to get to a big market you need to nail a series of small markets. Sure, they might have heard something about segmentation in a marketing class, but here you need to apply that from the very beginning.

The fix is to create a screener for each persona. This is a factual question whose job is specifically and only to determine whether a given subject does or does not map to your target persona. In the HVAC in a Hurry technician persona (see above), you might have a screening question like: ‘How many HVAC’s did you repair last week?’. If it’s <10, you might instead be talking to a handyman or a manager (or someone who’s not an HVAC tech at all).

And this is the point where (if I’ve made them comfortable enough to be candid with me) teams will ask me ‘But we want to go big- be the next Facebook.’ And then we talk about how just about all those success stories where there’s a product that has for all intents and purpose a universal user base started out by killing it in small, specific segments and learning and growing from there.

Sorry for all that, reader, but I find all this so frequently at this point and it’s so crucial to what I think is a healthy practice of HDD it seemed necessary.

The key with the interview guide is to start with general questions where you’re testing for a specific answer and then progressively get into more specific questions. Here are some resources–

An example interview guide related to the previous tutorials A general take on these interviews in the context of a larger customer discovery/design research program A template for drafting an interview guide

To recap, what’s a ‘Right Problem’ hypothesis? The Right Problem (persona and PS/JTBD) hypothesis is the most fundamental, but the hardest to pin down. You should know what kind of shoes your customer wears and when and why they use your product. You should be able to apply factual screeners to identify subjects that map to your persona or personas.

You should know what people who look like/behave like your customer who don’t use your product are doing instead, particularly if you’re in an industry undergoing change. You should be analyzing your quantitative data with strong, specific, emphatic hypotheses.

If you make software for HVAC (heating, ventilation and air conditioning) technicians, you should have a decent idea of what you’re likely to hear if you ask such a person a question like ‘What are the top 5 hardest things about finishing an HVAC repair?’

In summary, HDD here looks something like this:

01 IDEA : The working idea is that you know your customer and you’re solving a problem/doing a job (whatever term feels like it fits for you) that is important to them. If this isn’t the case, everything else you’re going to do isn’t going to matter.

Also, you know the top alternatives, which may or may not be what you see as your direct competitors. This is important as an input into focused testing demand to see if you have the Right Solution.

02 HYPOTHESIS : If you ask non-leading questions (like ‘What are the top 5 hardest things about finishing an HVAC repair?’), then you should generally hear relatively similar responses.

03 EXPERIMENTAL DESIGN : You’ll want an Interview Guide and, critically, a screener. This is a factual question you can use to make sure any given subject maps to your persona. With the HVAC repair example, this would be something like ‘How many HVAC repairs have you done in the last week?’ where you’re expecting an answer >5. This is important because if your screener isn’t tight enough, your interview responses may not converge.

04 EXPERIMENTATION : Get out and interview some subjects- but with a screener and an interview guide. The resources above has more on this, but one key thing to remember is that the interview guide is a guide, not a questionnaire. Your job is to make the interaction as normal as possible and it’s perfectly OK to skip questions or change them. It’s also 1000% OK to revise your interview guide during the process.

05: PIVOT OR PERSEVERE : What did you learn? Was it consistent? Good results are: a) We didn’t know what was on their A-list and what alternatives they are using, but we do know. b) We knew what was on their A-list and what alternatives they are using- we were pretty much right (doesn’t happen as much as you’d think). c) Our interviews just didn’t work/converge. Let’s try this again with some changes (happens all the time to smart teams and is very healthy).

By this, I mean: How do you test whether you have demand for your proposition? How do you know whether it’s better enough at solving a problem (doing a job, etc.) than the current alternatives your target persona has available to them now?

If an existing team was going to pick one of these areas to start with, I’d pick this one. While they’ll waste time if they haven’t found the right problem to solve and, yes, usability does matter, in practice this area of HDD is a good forcing function for really finding out what the team knows vs. doesn’t. This is why I show it as a kind of fulcrum between Right Problem and Right Solution:

This is not about usability and it does not involve showing someone a prototype, asking them if they like it, and checking the box.

Lean Startup offers a body of practice that’s an excellent fit for this. However, it’s widely misused because it’s so much more fun to build stuff than to test whether or not anyone cares about your idea. Yeah, seriously- that is the central challenge of Lean Startup.

Here’s the exciting part: You can massively improve your odds of success. While Lean Startup does not claim to be able to take any idea and make it successful, it does claim to minimize waste- and that matters a lot. Let’s just say that a new product or feature has a 1 in 5 chance of being successful. Using Lean Startup, you can iterate through 5 ideas in the space it would take you to build 1 out (and hope for the best)- this makes the improbably probable which is pretty much the most you can ask for in the innovation game .

Build, measure, learn, right? Kind of. I’ll harp on this since it’s important and a common failure mode relate to Lean Startup: an MVP is not a 1.0. As the Lean Startup folks (and Eric Ries’ book) will tell you, the right order is learn, build, measure. Specifically–

Learn: Who your customer is and what matters to them (see Solving the Right Problem, above). If you don’t do this, you’ll throwing darts with your eyes closed. Those darts are a lot cheaper than the darts you’d throw if you were building out the solution all the way (to strain the metaphor some), but far from free.

In particular, I see lots of teams run an MVP experiment and get confusing, inconsistent results. Most of the time, this is because they don’t have a screener and they’re putting the MVP in front of an audience that’s too wide ranging. A grandmother is going to respond differently than a millennial to the same thing.

Build : An experiment, not a real product, if at all possible (and it almost always is). Then consider MVP archetypes (see below) that will deliver the best results and try them out. You’ll likely have to iterate on the experiment itself some, particularly if it’s your first go.

Measure : Have metrics and link them to a kill decision. The Lean Startup term is ‘pivot or persevere’, which is great and makes perfect sense, but in practice the pivot/kill decisions are hard and as you decision your experiment you should really think about what metrics and thresholds are really going to convince you.

How do you code an MVP? You don’t. This MVP is a means to running an experiment to test motivation- so formulate your experiment first and then figure out an MVP that will get you the best results with the least amount of time and money. Just since this is a practitioner’s guide, with regard to ‘time’, that’s both time you’ll have to invest as well as how long the experiment will take to conclude. I’ve seen them both matter.

The most important first step is just to start with a simple hypothesis about your idea, and I like the form of ‘If we [do something] for [a specific customer/persona], then they will [respond in a specific, observable way that we can measure]. For example, if you’re building an app for parents to manage allowances for their children, it would be something like ‘If we offer parents and app to manage their kids’ allowances, they will download it, try it, make a habit of using it, and pay for a subscription.’

All that said, for getting started here is- A guide on testing with Lean Startup A template for creating motivation/demand experiments

To recap, what’s a Right Solution hypothesis for testing demand? The core hypothesis is that you have a value proposition that’s better enough than the target persona’s current alternatives that you’re going to acquire customers.

As you may notice, this creates a tight linkage with your testing from Solving the Right Problem. This is important because while testing value propositions with Lean Startup is way cheaper than building product, it still takes work and you can only run a finite set of tests. So, before you do this kind of testing I highly recommend you’ve iterated to validated learning on the what you see below: a persona, one or more PS/JTBD, the alternatives they’re using, and a testable view of why your VP is going to displace those alternatives. With that, your odds of doing quality work in this area dramatically increase!

What’s the testing, then? Well, it looks something like this:

01 IDEA : Most practicing scientists will tell you that the best way to get a good experimental result is to start with a strong hypothesis. Validating that you have the Right Problem and know what alternatives you’re competing against is critical to making investments in this kind of testing yield valuable results.

With that, you have a nice clear view of what alternative you’re trying to see if you’re better than.

02 HYPOTHESIS : I like a cause an effect stated here, like: ‘If we [offer something to said persona], they will [react in some observable way].’ This really helps focus your work on the MVP.

03 EXPERIMENTAL DESIGN : The MVP is a means to enable an experiment. It’s important to have a clear, explicit declaration of that hypothesis and for the MVP to delivery a metric for which you will (in advance) decide on a fail threshold. Most teams find it easier to kill an idea decisively with a kill metric vs. a success metric, even though they’re literally different sides of the same threshold.

04 EXPERIMENTATION : It is OK to tweak the parameters some as you run the experiment. For example, if you’re running a Google AdWords test, feel free to try new and different keyword phrases.

05: PIVOT OR PERSEVERE : Did you end up above or below your fail threshold? If below, pivot and focus on something else. If above, great- what is the next step to scaling up this proposition?

How does this related to usability? What’s usability vs. motivation? You might reasonably wonder: If my MVP has something that’s hard to understand, won’t that affect the results? Yes, sure. Testing for usability and the related tasks of building stuff are much more fun and (short-term) gratifying. I can’t emphasize enough how much harder it is for most founders, etc. is to push themselves to focus on motivation.

There’s certainly a relationship and, as we transition to the next section on usability, it seems like a good time to introduce the relationship between motivation and usability. My favorite tool for this is BJ Fogg’s Fogg Curve, which appears below. On the y-axis is motivation and on the x-axis is ‘ability’, the inverse of usability. If you imagine a point in the upper left, that would be, say, a cure for cancer where no matter if it’s hard to deal with you really want. On the bottom right would be something like checking Facebook- you may not be super motivated but it’s so easy.

The punchline is that there’s certainly a relationship but beware that for most of us our natural bias is to neglect testing our hypotheses about motivation in favor of testing usability.

First and foremost, delivering great usability is a team sport. Without a strong, co-created narrative, your performance is going to be sub-par. This means your developers, testers, analysts should be asking lots of hard, inconvenient (but relevant) questions about the user stories. For more on how these fit into an overall design program, let’s zoom out and we’ll again stand on the shoulders of Donald Norman.

Usability and User Cognition

To unpack usability in a coherent, testable fashion, I like to use Donald Norman’s 7-step model of user cognition:

The process starts with a Goal and that goals interacts with an object in an environment, the ‘World’. With the concepts we’ve been using here, the Goal is equivalent to a job-to-be-done. The World is your application in whatever circumstances your customer will use it (in a cubicle, on a plane, etc.).

The Reflective layer is where the customer is making a decision about alternatives for their JTBD/PS. In his seminal book, The Design of Everyday Things, Donald Normal’s is to continue reading a book as the sun goes down. In the framings we’ve been using, we looked at understanding your customers Goals/JTBD in ‘How do you test that you’ve found the ‘right problem’?’, and we looked evaluating their alternatives relative to your own (proposition) in ‘How do you test that you’ve found the ‘right solution’?’.

The Behavioral layer is where the user interacts with your application to get what they want- hopefully engaging with interface patterns they know so well they barely have to think about it. This is what we’ll focus on in this section. Critical here is leading with strong narrative (user stories), pairing those with well-understood (by your persona) interface patterns, and then iterating through qualitative and quantitative testing.

The Visceral layer is the lower level visual cues that a user gets- in the design world this is a lot about good visual design and even more about visual consistency. We’re not going to look at that in depth here, but if you haven’t already I’d make sure you have a working style guide to ensure consistency (see Creating a Style Guide ).

How do you unpack the UX Stack for Testability? Back to our example company, HVAC in a Hurry, which services commercial heating, ventilation, and A/C systems, let’s say we’ve arrived at the following tested learnings for Trent the Technician:

As we look at how we’ll iterate to the right solution in terms of usability, let’s say we arrive at the following user story we want to unpack (this would be one of many, even just for the PS/JTBD above):

As Trent the Technician, I know the part number and I want to find it on the system, so that I can find out its price and availability.

Let’s step through the 7 steps above in the context of HDD, with a particular focus on achieving strong usability.

1. Goal This is the PS/JTBD: Getting replacement parts to a job site. An HDD-enabled team would have found this out by doing customer discovery interviews with subjects they’ve screened and validated to be relevant to the target persona. They would have asked non-leading questions like ‘What are the top five hardest things about finishing an HVAC repair?’ and consistently heard that one such thing is sorting our replacement parts. This validates the PS/JTBD hypothesis that said PS/JTBD matters.

2. Plan For the PS/JTBD/Goal, which alternative are they likely to select? Is our proposition better enough than the alternatives? This is where Lean Startup and demand/motivation testing is critical. This is where we focused in ‘How do you test that you’ve found the ‘right solution’?’ and the HVAC in a Hurry team might have run a series of MVP to both understand how their subject might interact with a solution (concierge MVP) as well as whether they’re likely to engage (Smoke Test MVP).

3. Specify Our first step here is just to think through what the user expects to do and how we can make that as natural as possible. This is where drafting testable user stories, looking at comp’s, and then pairing clickable prototypes with iterative usability testing is critical. Following that, make sure your analytics are answering the same questions but at scale and with the observations available.

4. Perform If you did a good job in Specify and there are not overt visual problems (like ‘Can I click this part of the interface?’), you’ll be fine here.

5. Perceive We’re at the bottom of the stack and looping back up from World: Is the feedback from your application readily apparent to the user? For example, if you turn a switch for a lightbulb, you know if it worked or not. Is your user testing delivering similar clarity on user reactions?

6. Interpret Do they understand what they’re seeing? Does is make sense relative to what they expected to happen. For example, if the user just clicked ‘Save’, do they’re know that whatever they wanted to save is saved and OK? Or not?

7. Compare Have you delivered your target VP? Did they get what they wanted relative to the Goal/PS/JTBD?

How do you draft relevant, focused, testable user stories? Without these, everything else is on a shaky foundation. Sometimes, things will work out. Other times, they won’t. And it won’t be that clear why/not. Also, getting in the habit of pushing yourself on the relevance and testability of each little detail will make you a much better designer and a much better steward of where and why your team invests in building software.

For getting started here is- A guide on creating user stories A template for drafting user stories

How do you create find the relevant patterns and apply them? Once you’ve got great narrative, it’s time to put the best-understood, most expected, most relevant interface patterns in front of your user. Getting there is a process.

For getting started here is- A guide on interface patterns and prototyping

How do you run qualitative user testing early and often? Once you’ve got great something to test, it’s time to get that design in front of a user, give them a prompt, and see what happens- then rinse and repeat with your design.

For getting started here is- A guide on qualitative usability testing A template for testing your user stories

How do you focus your outcomes and instrument actionable observation? Once you release product (features, etc.) into the wild, it’s important to make sure you’re always closing the loop with analytics that are a regular part of your agile cadences. For example, in a high-functioning practice of HDD the team should be interested in and reviewing focused analytics to see how their pair with the results of their qualitative usability testing.

For getting started here is- A guide on quantitative usability testing with Google Analytics .

To recap, what’s a Right Solution hypothesis for usability? Essentially, the usability hypothesis is that you’ve arrived at a high-performing UI pattern that minimizes the cognitive load, maximizes the user’s ability to act on their motivation to connect with your proposition.

01 IDEA : If you’re writing good user stories , you already have your ideas implemented in the form of testable hypotheses. Stay focused and use these to anchor your testing. You’re not trying to test what color drop-down works best- you’re testing which affordances best deliver on a given user story.

02 HYPOTHESIS : Basically, the hypothesis is that ‘For [x] user story, this interface pattern will perform will, assuming we supply the relevant motivation and have the right assessments in place.

03 EXPERIMENTAL DESIGN : Really, this means have a tests set up that, beyond working, links user stories to prompts and narrative which supply motivation and have discernible assessments that help you make sure the subject didn’t click in the wrong place by mistake.

04 EXPERIMENTATION : It is OK to iterate on your prototypes and even your test plan in between sessions, particularly at the exploratory stages.

05: PIVOT OR PERSEVERE : Did the patterns perform well, or is it worth reviewing patterns and comparables and giving it another go?

There’s a lot of great material and successful practice on the engineering management part of application development. But should you pair program? Do estimates or go NoEstimates? None of these are the right choice for every team all of the time. In this sense, HDD is the only way to reliably drive up your velocity, or f e . What I love about agile is that fundamental to its design is the coupling and integration of working out how to make your release content successful while you’re figuring out how to make your team more successful.

What does HDD have to offer application development, then? First, I think it’s useful to consider how well HDD integrates with agile in this sense and what existing habits you can borrow from it to improve your practice of HDD. For example, let’s say your team is used to doing weekly retrospectives about its practice of agile. That’s the obvious place to start introducing a retrospective on how your hypothesis testing went and deciding what that should mean for the next sprint’s backlog.

Second, let’s look at the linkage from continuous design. Primarily, what we’re looking to do is move fewer designs into development through more disciplined experimentation before we invest in development. This leaves the developers the do things better and keep the pipeline healthier (faster and able to produce more content or story points per sprint). We’d do this by making sure we’re dealing with a user that exists, a job/problem that exists for them, and only propositions that we’ve successfully tested with non-product MVP’s.

But wait– what does that exactly mean: ‘only propositions that we’ve successfully tested with non-product MVP’s’? In practice, there’s no such thing as fully validating a proposition. You’re constantly looking at user behavior and deciding where you’d be best off improving. To create balance and consistency from sprint to sprint, I like to use a ‘ UX map ‘. You can read more about it at that link but the basic idea is that for a given JTBD:VP pairing you map out the customer experience (CX) arc broken into progressive stages that each have a description, a dependent variable you’ll observe to assess success, and ideas on things (independent variables or ‘IV’s’) to test. For example, here’s what such a UX map might look like for HVAC in a Hurry’s work on the JTBD of ‘getting replacement parts to a job site’.

From there, how can we use HDD to bring better, more testable design into the development process? One thing I like to do with user stories and HDD is to make a habit of pairing every single story with a simple, analytical question that would tell me whether the story is ‘done’ from the standpoint of creating the target user behavior or not. From there, I consider focal metrics. Here’s what that might look like at HinH.

For the last couple of decades, test and deploy/ops was often treated like a kind of stepchild to the development- something that had to happen at the end of development and was the sole responsibility of an outside group of specialists. It didn’t make sense then, and now an integral test capability is table stakes for getting to a continuous product pipeline, which at the core of HDD itself.

A continuous pipeline means that you release a lot. Getting good at releasing relieves a lot of energy-draining stress on the product team as well as creating the opportunity for rapid learning that HDD requires. Interestingly, research by outfits like DORA (now part of Google) and CircleCI shows teams that are able to do this both release faster and encounter fewer bugs in production.

Amazon famously releases code every 11.6 seconds. What this means is that a developer can push a button to commit code and everything from there to that code showing up in front of a customer is automated. How does that happen? For starters, there are two big (related) areas: Test & Deploy.

While there is some important plumbing that I’ll cover in the next couple of sections, in practice most teams struggle with test coverage. What does that mean? In principal, what it means is that even though you can’t test everything, you iterate to test automation coverage that is catching most bugs before they end up in front of a user. For most teams, that means a ‘pyramid’ of tests like you see here, where the x-axis the number of tests and the y-axis is the level of abstraction of the tests.

The reason for the pyramid shape is that the tests are progressively more work to create and maintain, and also each one provides less and less isolation about where a bug actually resides. In terms of iteration and retrospectives, what this means is that you’re always asking ‘What’s the lowest level test that could have caught this bug?’.

Unit tests isolate the operation of a single function and make sure it works as expected. Integration tests span two functions and system tests, as you’d guess, more or less emulate the way a user or endpoint would interact with a system.

Feature Flags: These are a separate but somewhat complimentary facility. The basic idea is that as you add new features, they each have a flag that can enable or disable them. They are start out disabled and you make sure they don’t break anything. Then, on small sets of users, you can enable them and test whether a) the metrics look normal and nothing’s broken and, closer to the core of HDD, whether users are actually interacting with the new feature.

In the olden days (which is when I last did this kind of thing for work), if you wanted to update a web application, you had to log in to a server, upload the software, and then configure it, maybe with the help of some scripts. Very often, things didn’t go accordingly to plan for the predictable reason that there was a lot of opportunity for variation between how the update was tested and the machine you were updating, not to mention how you were updating.

Now computers do all that- but you still have to program them. As such, the job of deployment has increasingly become a job where you’re coding solutions on top of platforms like Kubernetes, Chef, and Terraform. These folks are (hopefully) working closely with developers on this. For example, rather than spending time and money on writing documentation for an upgrade, the team would collaborate on code/config. that runs on the kind of application I mentioned earlier.

Pipeline Automation

Most teams with a continuous pipeline orchestrate something like what you see below with an application made for this like Jenkins or CircleCI. The Manual Validation step you see is, of course, optional and not a prevalent part of a truly continuous delivery. In fact, if you automate up to the point of a staging server or similar before you release, that’s what’s generally called continuous integration.

Finally, the two yellow items you see are where the team centralizes their code (version control) and the build that they’re taking from commit to deploy (artifact repository).

To recap, what’s the hypothesis?

Well, you can’t test everything but you can make sure that you’re testing what tends to affect your users and likewise in the deployment process. I’d summarize this area of HDD as follows:

01 IDEA : You can’t test everything and you can’t foresee everything that might go wrong. This is important for the team to internalize. But you can iteratively, purposefully focus your test investments.

02 HYPOTHESIS : Relative to the test pyramid, you’re looking to get to a place where you’re finding issues with the least expensive, least complex test possible- not an integration test when a unit test could have caught the issue, and so forth.

03 EXPERIMENTAL DESIGN : As you run integrations and deployments, you see what happens! Most teams move from continuous integration (deploy-ready system that’s not actually in front of customers) to continuous deployment.

04 EXPERIMENTATION : In retrospectives, it’s important to look at the tests suite and ask what would have made the most sense and how the current processes were or weren’t facilitating that.

05: PIVOT OR PERSEVERE : It takes work, but teams get there all the time- and research shows they end up both releasing more often and encounter fewer production bugs, believe it or not!

Topline, I would say it’s a way to unify and focus your work across those disciplines. I’ve found that’s a pretty big deal. While none of those practices are hard to understand, practice on the ground is patchy. Usually, the problem is having the confidence that doing things well is going to be worthwhile, and knowing who should be participating when.

My hope is that with this guide and the supporting material (and of course the wider body of practice), that teams will get in the habit of always having a set of hypotheses and that will improve their work and their confidence as a team.

Naturally, these various disciplines have a lot to do with each other, and I’ve summarized some of that here:

Mostly, I find practitioners learn about this through their work, but I’ll point out a few big points of intersection that I think are particularly notable:

Learn by Observing Humans We all tend to jump on solutions and over invest in them when we should be observing our user, seeing how they behave, and then iterating. HDD helps reinforce problem-first diagnosis through its connections to relevant practice.
Focus on What Users Actually Do A lot of thing might happen- more than we can deal with properly. The goods news is that by just observing what actually happens you can make things a lot easier on yourself.
Move Fast, but Minimize Blast Radius Working across so many types of org’s at present (startups, corporations, a university), I can’t overstate how important this is and yet how big a shift it is for more traditional organizations. The idea of ‘moving fast and breaking things’ is terrifying to these places, and the reality is with practice you can move fast and rarely break things/only break them a tiny bit. Without this, you end up stuck waiting for someone else to create the perfect plan or for that next super important hire to fix everything (spoiler: it won’t and they don’t).
Minimize Waste Succeeding at innovation is improbable, and yet it happens all the time. Practices like Lean Startup do not warrant that by following them you’ll always succeed; however, they do promise that by minimizing waste you can test five ideas in the time/money/energy it would otherwise take you to test one, making the improbable probable.

What I love about Hypothesis-Driven Development is that it solves a really hard problem with practice: that all these behaviors are important and yet you can’t learn to practice them all immediately. What HDD does is it gives you a foundation where you can see what’s similar across these and how your practice in one is reenforcing the other. It’s also a good tool to decide where you need to focus on any given project or team.

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How to Implement Hypothesis-Driven Development

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Remember back to the time when we were in high school science class. Our teachers had a framework for helping us learn – an experimental approach based on the best available evidence at hand. We were asked to make observations about the world around us, then attempt to form an explanation or hypothesis to explain what we had observed. We then tested this hypothesis by predicting an outcome based on our theory that would be achieved in a controlled experiment – if the outcome was achieved, we had proven our theory to be correct.

We could then apply this learning to inform and test other hypotheses by constructing more sophisticated experiments, and tuning, evolving, or abandoning any hypothesis as we made further observations from the results we achieved.

Experimentation is the foundation of the scientific method, which is a systematic means of exploring the world around us. Although some experiments take place in laboratories, it is possible to perform an experiment anywhere, at any time, even in software development.

Practicing Hypothesis-Driven Development [1] is thinking about the development of new ideas, products, and services – even organizational change – as a series of experiments to determine whether an expected outcome will be achieved. The process is iterated upon until a desirable outcome is obtained or the idea is determined to be not viable.

We need to change our mindset to view our proposed solution to a problem statement as a hypothesis, especially in new product or service development – the market we are targeting, how a business model will work, how code will execute and even how the customer will use it.

We do not do projects anymore, only experiments. Customer discovery and Lean Startup strategies are designed to test assumptions about customers. Quality Assurance is testing system behavior against defined specifications. The experimental principle also applies in Test-Driven Development – we write the test first, then use the test to validate that our code is correct, and succeed if the code passes the test. Ultimately, product or service development is a process to test a hypothesis about system behavior in the environment or market it is developed for.

The key outcome of an experimental approach is measurable evidence and learning. Learning is the information we have gained from conducting the experiment. Did what we expect to occur actually happen? If not, what did and how does that inform what we should do next?

In order to learn we need to use the scientific method for investigating phenomena, acquiring new knowledge, and correcting and integrating previous knowledge back into our thinking.

As the software development industry continues to mature, we now have an opportunity to leverage improved capabilities such as Continuous Design and Delivery to maximize our potential to learn quickly what works and what does not. By taking an experimental approach to information discovery, we can more rapidly test our solutions against the problems we have identified in the products or services we are attempting to build. With the goal to optimize our effectiveness of solving the right problems, over simply becoming a feature factory by continually building solutions.

The steps of the scientific method are to:

Make observations
Formulate a hypothesis
Design an experiment to test the hypothesis
State the indicators to evaluate if the experiment has succeeded
Conduct the experiment
Evaluate the results of the experiment
Accept or reject the hypothesis
If necessary, make and test a new hypothesis

Using an experimentation approach to software development

We need to challenge the concept of having fixed requirements for a product or service. Requirements are valuable when teams execute a well known or understood phase of an initiative and can leverage well-understood practices to achieve the outcome. However, when you are in an exploratory, complex and uncertain phase you need hypotheses. Handing teams a set of business requirements reinforces an order-taking approach and mindset that is flawed. Business does the thinking and ‘knows’ what is right. The purpose of the development team is to implement what they are told. But when operating in an area of uncertainty and complexity, all the members of the development team should be encouraged to think and share insights on the problem and potential solutions. A team simply taking orders from a business owner is not utilizing the full potential, experience and competency that a cross-functional multi-disciplined team offers.

Framing Hypotheses

The traditional user story framework is focused on capturing requirements for what we want to build and for whom, to enable the user to receive a specific benefit from the system.

As A…. <role>

I Want… <goal/desire>

So That… <receive benefit>

Behaviour Driven Development (BDD) and Feature Injection aims to improve the original framework by supporting communication and collaboration between developers, tester and non-technical participants in a software project.

In Order To… <receive benefit>

As A… <role>

When viewing work as an experiment, the traditional story framework is insufficient. As in our high school science experiment, we need to define the steps we will take to achieve the desired outcome. We then need to state the specific indicators (or signals) we expect to observe that provide evidence that our hypothesis is valid. These need to be stated before conducting the test to reduce the bias of interpretation of results.

If we observe signals that indicate our hypothesis is correct, we can be more confident that we are on the right path and can alter the user story framework to reflect this.

Therefore, a user story structure to support Hypothesis-Driven Development would be;

We believe < this capability >

What functionality we will develop to test our hypothesis? By defining a ‘test’ capability of the product or service that we are attempting to build, we identify the functionality and hypothesis we want to test.

Will result in < this outcome >

What is the expected outcome of our experiment? What is the specific result we expect to achieve by building the ‘test’ capability?

We will have confidence to proceed when < we see a measurable signal >

What signals will indicate that the capability we have built is effective? What key metrics (qualitative or quantitative) we will measure to provide evidence that our experiment has succeeded and give us enough confidence to move to the next stage.

The threshold you use for statistical significance will depend on your understanding of the business and context you are operating within. Not every company has the user sample size of Amazon or Google to run statistically significant experiments in a short period of time. Limits and controls need to be defined by your organization to determine acceptable evidence thresholds that will allow the team to advance to the next step.

For example, if you are building a rocket ship you may want your experiments to have a high threshold for statistical significance. If you are deciding between two different flows intended to help increase user sign up you may be happy to tolerate a lower significance threshold.

The final step is to clearly and visibly state any assumptions made about our hypothesis, to create a feedback loop for the team to provide further input, debate, and understanding of the circumstance under which we are performing the test. Are they valid and make sense from a technical and business perspective?

Hypotheses, when aligned to your MVP, can provide a testing mechanism for your product or service vision. They can test the most uncertain areas of your product or service, in order to gain information and improve confidence.

Examples of Hypothesis-Driven Development user stories are;

Business story.

We Believe That increasing the size of hotel images on the booking page Will Result In improved customer engagement and conversion We Will Have Confidence To Proceed When we see a 5% increase in customers who review hotel images who then proceed to book in 48 hours.

It is imperative to have effective monitoring and evaluation tools in place when using an experimental approach to software development in order to measure the impact of our efforts and provide a feedback loop to the team. Otherwise, we are essentially blind to the outcomes of our efforts.

In agile software development, we define working software as the primary measure of progress. By combining Continuous Delivery and Hypothesis-Driven Development we can now define working software and validated learning as the primary measures of progress.

Ideally, we should not say we are done until we have measured the value of what is being delivered – in other words, gathered data to validate our hypothesis.

Examples of how to gather data is performing A/B Testing to test a hypothesis and measure to change in customer behavior. Alternative testings options can be customer surveys, paper prototypes, user and/or guerilla testing.

One example of a company we have worked with that uses Hypothesis-Driven Development is lastminute.com . The team formulated a hypothesis that customers are only willing to pay a max price for a hotel based on the time of day they book. Tom Klein, CEO and President of Sabre Holdings shared the story of how they improved conversion by 400% within a week.

Combining practices such as Hypothesis-Driven Development and Continuous Delivery accelerates experimentation and amplifies validated learning. This gives us the opportunity to accelerate the rate at which we innovate while relentlessly reducing costs, leaving our competitors in the dust. Ideally, we can achieve the ideal of one-piece flow: atomic changes that enable us to identify causal relationships between the changes we make to our products and services, and their impact on key metrics.

As Kent Beck said, “Test-Driven Development is a great excuse to think about the problem before you think about the solution”. Hypothesis-Driven Development is a great opportunity to test what you think the problem is before you work on the solution.

We also run a workshop to help teams implement Hypothesis-Driven Development . Get in touch to run it at your company.

[1] Hypothesis-Driven Development By Jeffrey L. Taylor

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Data-driven hypothesis development

Have you ever tried solving a difficult problem with no clear path forward? Perhaps it’s a problem that may or may not be well understood or it’s a problem with many ideas of things that might work, and you are facing this without an approach to guide you. We've been there and lived this very scenario and will take you through an approach we've found to be very effective.

As Donald Rumsfeld once said, the problems we solve everyday can be classified into four categories:

“There are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns — the ones we don’t know we don’t know.”

Problems can be classified into four categories.

When working on problems with little data and high levels of risks (i.e. the “known unknowns” and “unknown unknowns”), it’s important to focus on finding the shortest path to the ‘correct’ solutions and removing the ‘incorrect’ solutions as soon as possible. In our experience, the best approach for solving these problems is to use hypotheses to focus your thinking and inform your decisions with data, which is known as: data-driven hypothesis development.

What is data-driven hypothesis development?

Data-driven hypothesis development (DDHD) is an effective approach when facing complex “known unknown” and “unknown unknown” problems. There are four steps to the approach:

1. Define the goal using data

Problem solving starts with well defined problems, however, we know that “known unknowns” and “unknown unknowns” are rarely well defined. Most of the time, the only thing we do know is that the system is broken and has many problems; what we don’t know is which problem is critical to help us achieve the strategic business goal.

The key is to define the problem using data, thus bringing the needed clarity. State the problem, define the metrics upfront and align these with your goals.

Setup a dashboard to visualize and track all key metrics.

2. Hypothesize

Hypotheses are introduced to create a path to the next state. This requires a change in mindset; proposed solutions are viewed as a series of experiments done in rapid iterations until a desirable outcome is achieved or the experiment is proved not viable.

One hypothesis is made up of one or many experiments. Each experiment is independent with a clear outcome, criteria and metrics. It should be short to build and short to test and learn. Results should be a clear indicator of success or not.

If the result of the experiment has a positive impact on the outcome, the next step would be to implement the change in production.

If an experiment is proved not viable, mark it as a failure, track and share lessons learned.

Capability to fail fast is pivotal. As we don’t know the exact path to the destination, we need to have the ability to quickly test different paths to effectively identify the next experiment.

Each hypothesis needs to be able to answer the question: when should we stop? At what point will you have enough information to make an informed decision?

3. Fast feedback

Experiments need to be small, specific, so that we can receive feedback in days rather than weeks. There are at least two feedback loops to build in when there is code change involved:

An isolated testing environment: to run the same set of testing suites to baseline the metrics and compare them with our experiment’s results

The production environment: once the experiment is proven in the testing environment it needs to be further tested in a production environment.

Fast feedback delivered through feedback loops is critical in determining the next step.

Fast feedback requires solid engineering practices like continuous delivery to accelerate experimentation and maximize what we can learn. We call out a few practices as an example, different systems might require different techniques:

Regression testing automation: for an orphaned legacy system, it’s important to build a regression testing suite as the learning progresses (have a baseline first then evolve as you go), providing a safety net and early feedback if any change is wrong.

Monitoring and observability: monitoring is quite often a big gap in legacy systems, not to mention observability. Start with monitoring, you will learn how the system is functioning, utilizing resources, when it will break and how it will behave in failure modes.

Performance testing automation: when there’s a problem about performance, there is a need to automate the performance testing so you can baseline the problem and continuously run it with every change.

A/B testing in production: set up the system to have basic ability to run the current system and the change in parallel; and rollback automatically if there is a need.

4. Incremental delivery of value

The value created by experiments, successful and failed, can be divided into three categories:

Tangible improvements on the system

Increased understanding of the problem and more data-informed decisions

Improved understanding of system via documentation, monitoring, test and etc.

It’s easy to take successful experiments as the only value delivered. Yet in the complex world of “known unknowns” and “unknown unknowns”, the value of “failed experiments” is equally important, providing clarity in decision making.

Another often ignored value delivered is the understanding of the problem/system, using data. This is extremely useful when there’s heavy loss of domain knowledge, providing a low cost, less risky way to rebuild knowledge.

Data-driven hypothesis development enables you to find the shortest path to your desired outcome, in itself delivering value.

Data driven hypothesis development approach

When facing a complex problem with many known unknowns and unknown unknowns, being data-driven serves as a compass, helping the team stay focused and prioritizing the right work. A data-driven approach helps you deliver incremental value and let’s you know when to stop.

Why we decided to use data-driven hypothesis development

Our client presented us with a proposed solution — a rebuild —- asking us to implement their shiny new design, believing this approach would solve the problem. However, it wasn’t clear what problem we’d be solving by implementing a new build, so we tried to better understand the problem to know if the proposed solution was likely to succeed.

Once we looked at the underlying architecture and discussed how we might do it differently, we discovered there wasn’t a lot we would change. The architecture at its core was solid, however, there were just too many layers of band-aid fixes. There was low visibility, the system was poorly understood and it had been neglected for many years.

DDHD would allow us to run short experiments, to learn as we delivered incremental value to the customer, and to continuously apply our lessons learned to have greater impact and rebuild domain knowledge.

Indicators data-driven hypothesis development might work for you

No or low visibility of the system and the problem

Little knowledge of the system exists within your organization

The system has been neglected for some time with band-aids applied loosely

You don’t know what to fix or where to start

You want to de-risk a large piece of work

You want to deliver value incrementally

You are looking at a complete rebuild as the solution

Our approach

1. understand the problem and explore all options.

To understand all sides of the problem, consider the underlying architecture, the customer, the business, and the issues being surfaced. One activity we ran recorded every known problem, discussing what we knew or didn’t know about it. This process involved people outside the immediate team. We gathered anyone who might have some knowledge on the system to join the problem discussion.

Once you have an understanding of the problem, share it far and wide. This is the beginning of your story; you will keep telling this story with data throughout the process, building interest, buy-in, support and knowledge.

The framework we used to guide us in our problem discussion.

2. Define the goals using data

As a team, define your goals or the desired outcomes. What is it you want to achieve? Discuss how you will measure success. What are the key metrics you will use? What does success look like? Once you’ve reached agreement on this, you’ll need to set about baselining your metrics.

We used a template similar to the one above to share our goals and record the metrics. The goals were front and center in our daily activities, we talked about them in stand-up, included them on story cards and shared them in our showcases, helping to anchor our thoughts and hold our focus. In an ideal world, you’ll see a direct line from your goal through to your organization's overarching objectives.

3. Hypothesize

One of the reasons we were successful in solving the problem and delivering outstanding results for our client was due to involving the whole team. We didn’t have just one or two team members writing hypotheses, defining, and driving experiments - every single member of the team was involved. To set your team up for success, align on the approach and how you’ll work together. Empower your team to write hypotheses from day one, no matter their role.

A table setting the goals, the approach, and what to deliver

We created templates to work from and encouraged pairing on writing hypotheses and defining experiments.

4. Experiment

Run small, data-driven experiments. One hypothesis can have one or many experiments. Experiments should be short to build and short to test. They should be independent and must be measurable.

5. Conclude the experiment

Acceptance criteria plays a critical role in determining whether the experiment is successful or not. For successful experiments, we will need to build a plan to apply the changes. For all experiments, successful or not, you should revisit other remaining experiments with the new data you have collected and change accordingly upon completion. This could mean updating, stopping or creating new experiments.

Every conclusion of an experiment is a starting point of the next step plan.

6. Track the experiment and share results

Use data to tell stories and share your lessons learned. Don’t just share this with your immediate team; share your lessons learned and data with the business and your stakeholders. The more they know, the more empowered they will feel too. Take people on the journey with you. Build an experiment dashboard and use it as an info radar to visualize the learning.

Key takeaways

Our key takeaways from running a DDHD approach:

Use data to tell stories. Data was key in all of this. We used it in every conversation, every showcase, every brainstorming session. Data helped us to align the business, get buy-in from stakeholders, empower the team, and to celebrate wins.

De-risk a large piece of work. We were asking our clients to trust us to fix the “unknown unknowns” over implementing a shiny new solution. DDHD enabled us to deliver incremental value, gaining trust each week and de-risking a piece of work with a potential 12 - 24 month timeframe and equally big price tag.

Be comfortable with failure. We encouraged the team to celebrate the failed experiments as much as the successful ones. Lessons come from failure, failure enables decision making and through this we find the quickest path to the desired outcome.

Empower the team to own the problem and the goals. Our success was a direct result of the whole team taking ownership of the problem and the goals. The team were empowered early on to form hypotheses and write experiments. Everytime they learned something, it was shared back and new hypotheses and/or experiments were formed.

Deliver incremental parcels of value. Keep focussed on delivering small, incremental changes. When faced with a large piece of work and/or a system that has been neglected for some time, it can feel impossible to have an impact. We focussed on delivering value weekly. Delivering value wasn’t just about getting something into the customers’ hands, it was also learning from failed experiments. Celebrate every step, it means you are inching closer to success.

We’ve found this to be a really valuable approach to dealing with problems that can be classified as ‘known unknowns’ and ‘unknown unknowns’ and we hope you find this technique useful too.

Let's talk about your next project

Need To Solve a Complex Problem? Use This Powerful Hypothesis-Driven Approach

A hypothesis-driven approach is the secret to solving any complex business problem — even the most convoluted and confusing ones.

If you run a business, you’re going to face complicated scenarios. Simple as that.

I know the big problems can feel overwhelming and unmanageable in the moment. You’re already juggling five thousand responsibilities. Adding ‘ understand why our profits have flatlined the past three months’ might feel like one ball too many to handle.

Luckily, there’s a proven way to approach your problems — a way that allows you to use data-driven insights to more effectively overcome any business dilemma.

Hypothesis-driven problem solving: A proven approach to complex problems

Are you familiar with McKinsey ? The management consulting company helps some of the world’s most influential businesses solve complex problems using a hypothesis-driven approach.

Now, organisations pay McKinsey consultants top dollar because their problem-solving process works . No matter the size of your business, I believe that’s worth paying attention to…

In this article, we’ll explore what it means to be hypothesis-driven in your business. Then, I’ll outline a five-step process to problem-solving to help you overcome even your trickiest business challenges.

What is a hypothesis-driven approach to problem solving?

Think back to grade school science class. Do you remember formulating a hypothesis before conducting an experiment?

A hypothesis-driven approach to problem solving isn’t all that different.

When faced with a complex business problem, we want to make an initial hypothesis. In other words, what is causing the problem?

We then conduct experiments to test the hypothesis. Using the data gained, we create a step-by-step solution. Finally, we reiterate until we solve the problem.

Sounds straightforward, right? In a way, it is — so long as you follow the right steps to formulate and test your hypothesis. Here’s what that looks like:

Solve any complex business problem with this 5-step hypothesis-driven approach

1. gather your initial facts.

Before you craft your initial hypothesis, take a few moments to gather your facts. We’re not unearthing every piece of data possible — that would be time-consuming and counter-productive.

However, we do want to ensure we understand the full scope of the problem and the most critical factors affecting the situation.

Consider (1) What is the exact problem I want to solve and (2) what does the data I have already say about the problem?

2. Break down your complex problem

“ Complexity is your enemy. Any fool can make something complicated. It is hard to make something simple. ” - Richard Branson.

Big problems are daunting. Smaller micro-problems are less intimidating and easier to handle. So, simplify your complex problems.

McKinsey consultants use two frameworks to break larger problems into smaller, more manageable pieces:

Issue trees

An issue tree is a visual breakdown of a question or problem into its essential elements. It maps out your problem clearly and concisely while accounting for all possible issues.

This is an excellent article that explains in great detail how to structure issue trees for your hypothesis-driven approaches.

The MECE Principle

MECE means Mutually Exclusive, Collectively Exhaustive. The basic idea is that your issue tree has no gaps (it is collectively exhaustive) and no overlaps (it is mutually exclusive; each part is different from the rest.)

Once you’ve created your issue tree following the MECE principle, you’ll better understand the full scope of your problem. You’ll also discover new, creative ideas you have yet to consider.

3. Define your initial hypothesis

Now that you fully understand the facts and issues surrounding your problem, make a clear and testable hypothesis.

Zoom out from your issue tree so that you once again see the full scope of the problem; use this bird’s eye view to craft your hypothesis-driven approach.

This initial hypothesis is your guide — it will keep you laser-focused and pointed in the right direction so that you solve your problem without any unnecessary detours.

That’s not to say your initial hypothesis will always be correct. It won’t. We still need to conduct a fact-based analysis to determine its validity.

However, the beauty of an initial hypothesis is that it reinforces a solution-oriented mindset while preventing you from venturing down a veritable rabbit hole of extraneous facts and information.

4. Apply a test and learn approach

With our hypothesis in hand, it’s time to validate our ideas with ‘test and learn marketing’.

In other words, we want to test our hypothesis to obtain data-driven insights that will prove (or disprove) our initial hypothesis so we can make the necessary improvements.

One of the primary benefits of a test and learn approach to problem solving is that we don’t rely on guesswork and hope for the best. Instead, we use the data to evaluate our hypothesis so that we quickly zero in on what works (and incrementally increase our efforts from there.)

Note: You’ll want to define your KPIs so that your team stays focused on the metrics that matter. Have specific, measurable goals and targets in place.

5. Return to your initial hypothesis

You’ve tested your hypothesis. Now, what do the experiment results tell you?

Here’s what I want you to remember:

Growth marketers who embrace a hypothesis-driven approach to problem solving are not intimidated by failure.

Stay unattached to the results of your experiment. It’s not about proving the hypothesis correct but about quickly failing (so that we can succeed even more rapidly.) A famous quote by Thomas Edison comes to mind:

“I have not failed. I’ve just found 10,000 ways that won’t work. ”

Thanks to new technology , it’s never been easier to use data to solve problems. The key is to instil a test and learn culture within your business so your employees stay agile while using hypotheses to approach problem solving.

So, encourage curiosity, apply results, and make decisions based on the story the data tells .

You’ll implement more of what works and quickly ditch what doesn’t so you can grow your bottom line more rapidly.

Curious about how we can help

Grow your business, get in touch..

InVisionApp, Inc.

Inside Design

5 steps to a hypothesis-driven design process

• mar 22, 2018.

S ay you’re starting a greenfield project, or you’re redesigning a legacy app. The product owner gives you some high-level goals. Lots of ideas and questions are in your mind, and you’re not sure where to start.

Hypothesis-driven design will help you navigate through a unknown space so you can come out at the end of the process with actionable next steps.

Ready? Let’s dive in.

Step 1: Start with questions and assumptions

On the first day of the project, you’re curious about all the different aspects of your product. “How could we increase the engagement on the homepage? ” “ What features are important for our users? ”

Related: 6 ways to speed up and improve your product design process

To reduce risk, I like to take some time to write down all the unanswered questions and assumptions. So grab some sticky notes and write all your questions down on the notes (one question per note).

I recommend that you use the How Might We technique from IDEO to phrase the questions and turn your assumptions into questions. It’ll help you frame the questions in a more open-ended way to avoid building the solution into the statement prematurely. For example, you have an idea that you want to make riders feel more comfortable by showing them how many rides the driver has completed. You can rephrase the question to “ How might we ensure rider feel comfortable when taking ride, ” and leave the solution part out to the later step.

“It’s easy to come up with design ideas, but it’s hard to solve the right problem.”

It’s even more valuable to have your team members participate in the question brainstorming session. Having diverse disciplines in the room always brings fresh perspectives and leads to a more productive conversation.

Step 2: Prioritize the questions and assumptions

Now that you have all the questions on sticky notes, organize them into groups to make it easier to review them. It’s especially helpful if you can do the activity with your team so you can have more input from everybody.

When it comes to choosing which question to tackle first, think about what would impact your product the most or what would bring the most value to your users.

If you have a big group, you can Dot Vote to prioritize the questions. Here’s how it works: Everyone has three dots, and each person gets to vote on what they think is the most important question to answer in order to build a successful product. It’s a common prioritization technique that’s also used in the Sprint book by Jake Knapp —he writes, “ The prioritization process isn’t perfect, but it leads to pretty good decisions and it happens fast. ”

Related: Go inside design at Google Ventures

Step 3: Turn them into hypotheses

After the prioritization, you now have a clear question in mind. It’s time to turn the question into a hypothesis. Think about how you would answer the question.

Let’s continue the previous ride-hailing service example. The question you have is “ How might we make people feel safe and comfortable when using the service? ”

Based on this question, the solutions can be:

Sharing the rider’s location with friends and family automatically
Displaying more information about the driver
Showing feedback from previous riders

Now you can combine the solution and question, and turn it into a hypothesis. Hypothesis is a framework that can help you clearly define the question and solution, and eliminate assumption.

From Lean UX

We believe that [ sharing more information about the driver’s experience and stories ] For [ the riders ] Will [ make riders feel more comfortable and connected throughout the ride ]

4. Develop an experiment and testing the hypothesis

Develop an experiment so you can test your hypothesis. Our test will follow the scientific methods, so it’s subject to collecting empirical and measurable evidence in order to obtain new knowledge. In other words, it’s crucial to have a measurable outcome for the hypothesis so we can determine whether it has succeeded or failed.

There are different ways you can create an experiment, such as interview, survey , landing page validation, usability testing, etc. It could also be something that’s built into the software to get quantitative data from users. Write down what the experiment will be, and define the outcomes that determine whether the hypothesis is valids. A well-defined experiment can validate/invalidate the hypothesis.

In our example, we could define the experiment as “ We will run X studies to show more information about a driver (number of ride, years of experience), and ask follow-up questions to identify the rider’s emotion associated with this ride (safe, fun, interesting, etc.). We will know the hypothesis is valid when we get more than 70% identify the ride as safe or comfortable. ”

After defining the experiment, it’s time to get the design done. You don’t need to have every design detail thought through. You can focus on designing what is needed to be tested.

When the design is ready, you’re ready to run the test. Recruit the users you want to target , have a time frame, and put the design in front of the users.

5. Learn and build

You just learned that the result was positive and you’re excited to roll out the feature. That’s great! If the hypothesis failed, don’t worry—you’ll be able to gain some insights from that experiment. Now you have some new evidence that you can use to run your next experiment. In each experiment, you’ll learn something new about your product and your customers.

“Design is a never-ending process.”

What other information can you show to make riders feel safe and comfortable? That can be your next hypothesis. You now have a feature that’s ready to be built, and a new hypothesis to be tested.

Principles from from The Lean Startup

We often assume that we understand our users and know what they want. It’s important to slow down and take a moment to understand the questions and assumptions we have about our product.

After testing each hypothesis, you’ll get a clearer path of what’s most important to the users and where you need to dig deeper. You’ll have a clear direction for what to do next.

by Sylvia Lai

Sylvia Lai helps startup and enterprise solve complex problems through design thinking and user-centered design methodologies at Pivotal Labs . She is the biggest advocate for the users, making sure their voices are heard is her number one priority. Outside of work, she loves mentoring other designers through one-on-one conversation. Connect with her through LinkedIn or Twitter .

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The 6 Steps that We Use for Hypothesis-Driven Development

One of the greatest fears of product managers is to create an app that flopped because it's based on untested assumptions. After successfully launching more than 20 products, we're convinced that we've found the right approach for hypothesis-driven development.

In this guide, I'll show you how we validated the hypotheses to ensure that the apps met the users' expectations and needs.

What is hypothesis-driven development?

Hypothesis-driven development is a prototype methodology that allows product designers to develop, test, and rebuild a product until it’s acceptable by the users. It is an iterative measure that explores assumptions defined during the project and attempts to validate it with users’ feedbacks.

What you have assumed during the initial stage of development may not be valid for the users. Even if they are backed by historical data, user behaviors can be affected by specific audiences and other factors. Hypothesis-driven development removes these uncertainties as the project progresses.

Why we use hypothesis-driven development

For us, the hypothesis-driven approach provides a structured way to consolidate ideas and build hypotheses based on objective criteria. It’s also less costly to test the prototype before production.

Using this approach has reliably allowed us to identify what, how, and in which order should the testing be done. It gives us a deep understanding of how we prioritise the features, how it’s connected to the business goals and desired user outcomes.

We’re also able to track and compare the desired and real outcomes of developing the features.

The process of Prototype Development that we use

Our success in building apps that are well-accepted by users is based on the Lean UX definition of hypothesis. We believe that the business outcome will be achieved if the user’s outcome is fulfilled for the particular feature.

Here’s the process flow:

How Might We technique → Dot voting (based on estimated/assumptive impact) → converting into a hypothesis → define testing methodology (research method + success/fail criteria) → impact effort scale for prioritizing → test, learn, repeat.

Once the hypothesis is proven right, the feature is escalated into the development track for UI design and development.

Step 1: List Down Questions And Assumptions

Whether it’s the initial stage of the project or after the launch, there are always uncertainties or ideas to further improve the existing product. In order to move forward, you’ll need to turn the ideas into structured hypotheses where they can be tested prior to production.

To start with, jot the ideas or assumptions down on paper or a sticky note.

Then, you’ll want to widen the scope of the questions and assumptions into possible solutions. The How Might We (HMW) technique is handy in rephrasing the statements into questions that facilitate brainstorming.

For example, if you have a social media app with a low number of users, asking, “How might we increase the number of users for the app?” makes brainstorming easier.

Step 2: Dot Vote to Prioritize Questions and Assumptions

Once you’ve got a list of questions, it’s time to decide which are potentially more impactful for the product. The Dot Vote method, where team members are given dots to place on the questions, helps prioritize the questions and assumptions.

Our team uses this method when we’re faced with many ideas and need to eliminate some of them. We started by grouping similar ideas and use 3-5 dots to vote. At the end of the process, we’ll have the preliminary data on the possible impact and our team’s interest in developing certain features.

This method allows us to prioritize the statements derived from the HMW technique and we’re only converting the top ones.

Step 3: Develop Hypotheses from Questions

The questions lead to a brainstorming session where the answers become hypotheses for the product. The hypothesis is meant to create a framework that allows the questions and solutions to be defined clearly for validation.

Our team followed a specific format in forming hypotheses. We structured the statement as follow:

We believe we will achieve [ business outcome],

If [ the persona],

Solve their need in [ user outcome] using [feature]. ‍

Here’s a hypothesis we’ve created:

We believe we will achieve DAU=100 if Mike (our proto persona) solve their need in recording and sharing videos instantaneously using our camera and cloud storage .

Step 4: Test the Hypothesis with an Experiment

It’s crucial to validate each of the assumptions made on the product features. Based on the hypotheses, experiments in the form of interviews, surveys, usability testing, and so forth are created to determine if the assumptions are aligned with reality.

Each of the methods provides some level of confidence. Therefore, you don’t want to be 100% reliant on a particular method as it’s based on a sample of users.

It’s important to choose a research method that allows validation to be done with minimal effort. Even though hypotheses validation provides a degree of confidence, not all assumptions can be tested and there could be a margin of error in data obtained as the test is conducted on a sample of people.

The experiments are designed in such a way that feedback can be compared with the predicted outcome. Only validated hypotheses are brought forward for development.

Testing all the hypotheses can be tedious. To be more efficient, you can use the impact effort scale. This method allows you to focus on hypotheses that are potentially high value and easy to validate.

You can also work on hypotheses that deliver high impact but require high effort. Ignore those that require high impact but low impact and keep hypotheses with low impact and effort into the backlog.

At Uptech, we assign each hypothesis with clear testing criteria. We rank the hypothesis with a binary ‘task success’ and subjective ‘effort on task’ where the latter is scored from 1 to 10.

While we’re conducting the test, we also collect qualitative data such as the users' feedback. We have a habit of segregation the feedback into pros, cons and neutral with color-coded stickers. (red - cons, green -pros, blue- neutral).

The best practice is to test each hypothesis at least on 5 users.

Step 5 Learn, Build (and Repeat)

The hypothesis-driven approach is not a single-ended process. Often, you’ll find that some of the hypotheses are proven to be false. Rather than be disheartened, you should use the data gathered to finetune the hypothesis and design a better experiment in the next phase.

Treat the entire cycle as a learning process where you’ll better understand the product and the customers.

We’ve found the process helpful when developing an MVP for Carbon Club, an environmental startup in the UK. The app allows users to donate to charity based on the carbon-footprint produced.

In order to calculate the carbon footprint, we’re weighing the options of

Connecting the app to the users’ bank account to monitor the carbon footprint based on purchases made.
Allowing users to take quizzes on their lifestyles.

Upon validation, we’ve found that all of the users opted for the second option as they are concerned about linking an unknown app to their banking account.

The result makes us shelves the first assumption we’ve made during pre-Sprint research. It also saves our client $50,000, and a few months of work as connecting the app to the bank account requires a huge effort.

Step 6: Implement Product and Maintain

Once you’ve got the confidence that the remaining hypotheses are validated, it’s time to develop the product. However, testing must be continued even after the product is launched.

You should be on your toes as customers’ demands, market trends, local economics, and other conditions may require some features to evolve.

Our takeaways for hypothesis-driven development

If there’s anything that you could pick from our experience, it’s these 5 points.

1. Should every idea go straight into the backlog? No, unless they are validated with substantial evidence.

2. While it’s hard to define business outcomes with specific metrics and desired values, you should do it anyway. Try to be as specific as possible, and avoid general terms. Give your best effort and adjust as you receive new data.

3. Get all product teams involved as the best ideas are born from collaboration.

4. Start with a plan consists of 2 main parameters, i.e., criteria of success and research methods. Besides qualitative insights, you need to set objective criteria to determine if a test is successful. Use the Test Card to validate the assumptions strategically.

5. The methodology that we’ve recommended in this article works not only for products. We’ve applied it at the end of 2019 for setting the strategic goals of the company and end up with robust results, engaged and aligned team.

You'll have a better idea of which features would lead to a successful product with hypothesis-driven development. Rather than vague assumptions, the consolidated data from users will provide a clear direction for your development team.

As for the hypotheses that don't make the cut, improvise, re-test, and leverage for future upgrades.

Keep failing with product launches? I'll be happy to point you in the right direction. Drop me a message here.

Tell us about your idea. We will reach you out.

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Hypothesis-Driven Development

Hypothesis-driven development (hdd), also known as hypothesis-driven product development, is an approach used in software development and product management..

HDD involves creating hypotheses about user behavior, needs, or desired outcomes, and then designing and implementing experiments to validate or invalidate those hypotheses.

Related blogs

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Data-driven innovation: If you want to innovate with data, this is what you should do!

Artificial intelligence life cycle

Why use a hypothesis-driven approach?

With hypothesis-driven development, instead of making assumptions and building products or features based on those assumptions, teams should formulate hypotheses and conduct experiments to gather data and insights.

This method assists with making informed decisions and reduces the overall risk of building products that do not meet user needs or solve their problems.

How do you implement hypothesis-driven development

At a high level, here’s a general approach to implementing HDD:

Identify the problem or opportunity: Begin by identifying the problem or opportunity that you want to address with your product or feature.
Create a hypothesis: Clearly define a hypothesis that describes a specific user behavior, need, or outcome you believe will occur if you implement the solution.
Design an experiment: Determine the best way to test your hypothesis. This could involve creating a prototype, conducting user interviews, A/B testing, or other forms of user research.
Implement the experiment: Execute the experiment by building the necessary components or conducting the research activities.
Collect and analyze data: Gather data from the experiment and analyze the results to determine if the hypothesis is supported or not.
If the hypothesis is supported, you can move forward with further development.
If the hypothesis is not supported, you may need to pivot, refine the hypothesis, or explore alternative solutions.
Rinse and repeat: Continuously repeat the process, iterating and refining your hypotheses and experiments to guide the development of your product or feature.

Hypothesis-driven development emphasizes a data-driven and iterative approach to product development, allowing teams to make more informed decisions, validate assumptions, and ultimately deliver products that better meet user needs.

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J Oral Maxillofac Pathol
v.23(2); May-Aug 2019

Hypothesis-driven Research

Umadevi krishnamohan rao.

1 Department of Oral and Maxillofacial Pathology, Ragas Dental College and Hospital, Chennai, Tamil Nadu, India E-mail: moc.liamg@kvuamu

An external file that holds a picture, illustration, etc.
Object name is JOMFP-23-168-g001.jpg

As Oral Pathologists, we have the responsibility to upgrade our quality of service with an open mind attitude and gratitude for the contributions made by our professional colleagues. Teaching the students is the priority of the faculty, and with equal priority, oral pathologists have the responsibility to contribute to the literature too as a researcher.

Research is a scientific method of answering a question. This can be achieved when the work done in a representative sample of the population, i.e., the outcome of the result, can be applied to the rest of the population, from which the sample is drawn. In this context, frequently done research is a hypothesis-driven research which is based on scientific theories. Specific aims are listed in this type of research, and the objectives are stated. The scope of a well-designed methodology in a hypothesis-driven research equips the researcher to establish an opportunity to state the outcome of the study.

A provisional statement in which the relationship between two variables is described is known as hypothesis. It is very specific and offers the freedom of evaluating a prediction between the variables stated. It facilitates the researcher to envision and gauge as to what changes can occur in the listed specific outcome variables (dependent) when changes are made in a specific predictor (independent) variable. Thus, any given hypothesis should include both these variables, and the primary aim of the study should be focused on demonstrating the association between the variables, by maintaining the highest ethical standards.

The other requisites for a hypothesis-based study are we should state the level of statistical significance and should specify the power, which is defined as the probability that a statistical test will indicate a significant difference when it truly exists.[ 1 ] In a hypothesis-driven research, specifications of methodology help the grant reviewers to differentiate good science from bad science, and thus, hypothesis-driven research is the most funded research.[ 2 ]

“Hypotheses aren’t simply useful tools in some potentially outmoded vision of science; they are the whole point.” This was stated by Sean Carroll, from the California Institute of Technology, in response to Editor-In-Chief of “ Wired ” Chris Anderson, who argued that “biology is too complex for hypotheses and models, and he favored working on enormous data by correlative analysis.”[ 3 ]

Research does not stop by stating the hypotheses but must ensure that it is clear, testable and falsifiable and should serve as the fundamental basis for constructing a methodology that will allow either its acceptance (study favoring a null hypothesis) or rejection (study rejecting the null hypothesis in favor of the alternative hypothesis).

It is very worrying to observe that many research projects, which require a hypothesis, are being done without stating one. This is the fundamental backbone of the question to be asked and tested, and later, the findings need to be extrapolated in an analytical study, addressing a research question.

A good dissertation or thesis which is submitted for fulfillment of a curriculum or a submitted manuscript is comprised of a thoughtful study, addressing an interesting concept, and has to be scientifically designed. Nowadays, evolving academicians are in a competition to prove their point and be academically visible, which is very vital in their career graph. In any circumstance, unscientific research or short-cut methodology should never be conducted or encouraged to produce a research finding or publish the same as a manuscript.

The other type of research is exploratory research, which is based on a journey for discovery, which is not backed by previously established theories and is driven by hope and chance of breakthrough. The advantage of using these data is that statistics can be applied to establish predictions without the consideration of the principles of designing a study, which is the fundamental requirement of a conventional hypothesis. There is a need to set standards of statistical evidence with a much higher cutoff value for acceptance when we consider doing a study without a hypothesis.

In the past few years, there is an emergence of nonhypothesis-driven research, which does receive encouragement from funding agencies such as innovative molecular analysis technologies. The point to be taken here is that funding of nonhypothesis-driven research does not implicate decrease in support to hypothesis-driven research, but the objective is to encourage multidisciplinary research which is dependent on coordinated and cooperative execution of many branches of science and institutions. Thus, translational research is challenging and does carry a risk associated with the lack of preliminary data to establish a hypothesis.[ 4 ]

The merit of hypothesis testing is that it takes the next stride in scientific theory, having already stood the rigors of examination. Hypothesis testing is in practice for more than five decades and is considered to be a standard requirement when proposals are being submitted for evaluation. Stating a hypothesis is mandatory when we intend to make the study results applicable. Young professionals must be appraised of the merits of hypothesis-based research and must be trained to understand the scope of exploratory research.

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Using a Hypothesis-Driven Approach in Analyzing (and Making Sense) of Your Website Traffic Data

At the Digital Analytics Program (DAP), some of the most frequently asked questions we get are “how can I get access to the DAP data?” and “what do I do with all this data?” We all know that data is knowledge, and knowledge is power, but once we have access to it and realize that it is, indeed, oceans of data , how do we not “drown” in it, and, perhaps more importantly, how do we make sense of it?

What Questions Are You Trying to Answer?

In research and data analysis, a hypothesis-driven approach is one of the main methods for using data to test and, ultimately, prove (or disprove) assertions. To do that, researchers collect a sufficient amount of data on the subject and then approach it with a specific hypothesis in mind. On the flipside, there’s also exploratory data analysis, where data analysts “dive” into data in search of patterns (or lack there of). Often times, exploratory data analysis creates a foundation for hypotheses, or feeds our assertions that then get used as part of hypothesis-driven testing.

So what does this all mean? It means that when you are attempting to perform an analysis using multidimensional website traffic data, you, too, should approach it with a goal in mind, or, at minimum, specific questions you want to be answered. Hence, when people ask us, “what do I do with all this data?” our response is “what questions are you trying to answer?”

Example: Real-Time Gov-Wide Visitors on #TaxDay

Let’s use this in a concrete example. On April 14, 2015, in a conversation with my team regarding the gov-wide website traffic on #TaxDay, I casually made an assertion that the number of real-time visitors on April 15, 2015, may reach 300,000. The assertion was based on the fact that in almost three years of the DAP’s life, we’ve seen several big spikes (up to 220K real-time visitors), which had happened before IRS.gov became part of the DAP. So, I figured, now that DAP monitors IRS.gov traffic, in addition to other large governement websites, perhaps we’ll see a big spike in real-time visitor traffic on #TaxDay because lots of people will be online filing their tax returns and/or extensions on the last day. My hypothesis was not based on any previously done exploratory analysis.

To see if we’d reach that new record of real-time traffic in DAP on #TaxDay, our DigitalGov team performed live blogging and hourly monitoring throughout the day yesterday to report on the real-time users on the .gov websites as part of the public dashboard . And…. the hypothesis did not hold true (and was rejected by reality). The highest number of real-time visitors gov-wide we got on #TaxDay for 2015 was just shy of 200K.

What Happens After the Hypothesis is Tested

So now that the results of my hypothesis are known, the big question is “why?”

Well, the data tells us that for the last three months, the “ Where’s My Refund ?” page has been consistently in the top performing pages with the highest number of real-time users and a total 115M+ pageviews since February 1, 2015. IRS-related pages overall have been dominating the top 20 active pages consistently in the last three months, suggesting that people were filing their taxes online during the last few months and, then, naturally, spent most of their time wondering when they get their refund. We did see a spike in the extension applications [PDF] downloads yesterday, which makes sense, but the number of visitors filling out the application was not high enough to bring us anywhere even close to 300K real-time visitors.

Interestingly, the uptick in overall traffic to .gov websites on #TaxDay was modest compared to previous events driven by weather, space shuttle launches, and asteroid fly-bys. Hmmm…. that may be a good topic for a different blog post.

In Conclusion

The DAP yields a lot of data and may be overwhelming but is a goldmine for exploratory and hypothesis-driven analyses. With the right questions in mind, this data can help the government to better understand its visitors and what brings them to agency websites, and ultimately, continuously create better web content and digital services for online visitors.

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How to be "hypothesis driven" what does is really mean.

I am not clear about how to be "hypothesis driven". In ideal scenario, a candidate should draw out issue trees, list key hypothesis ->test hypothes ->revise hypothesis. In real interview, is it possible to really make the whole case "hypothesis driven" and streamlined?

I've seen several ways of using hypothesis. Example one: key hypothesis that the client should enter Middle East's coffee market is that the market is attractive. To test the market's attractiveness, I need to look into several areas. First, market growth... Second, customer preference....etc. The above example to me sounds more "hypothesis driven".

Example two: state hypothesis of each branches of the issue tree. For instance: should high end European hair product manufacturer enter Asia market? One hypothesis (among many) is that our client is able to make a product that suits Asian customer's preference. I'm confused by this method. Although I may be mentioning many hypothesis when I mention each issue tree branch, I'm not using hypothesis to guide the "flow". I'm sharing hypothesis of many different topics.

Could you please share what would be the best way to use hypothesis in the case? Ideally, the way consultants would use hypothesis in every day life.

Overview of answers

Date ascending
Date descending

While I do highly recommend you get coaching for this (your question is essentially asking "How do I solve a case well"), I can provide some initial guidance here.

Hypothesis-driven - We are no longer singular hypothesis-driven , stating this boldly at the beginning of the case. Rather, we are hypotheses-driven or objective-driven . This is very different.

Your entire framework is a set of hypotheses and views as to how to solve a problem.

You don't need to state it explicitly, but remember that 1) You need to always be thinking about one and 2) You need to be demonstrating your drive towards one.

Also, remember that a hypothesis isn't necessarily "I believe x is the cause". Be better hypothesis is "If we can see what's happening with A, and A is going up, and then we look into B and B is big, then x is likely the case".

A hypothesis is much more about what questions do I need to ask/answer and how, in order to see what's happening.

Another way of viewing it:

Your framework is your structure for approaching the problem. It consits of a few main areas you'd like to look at. Inherent in your framework is a view that "If I answer A, B, and C, then we have an answer"

So, for market entry:

1) If the market is big, and it's growing, then we still want to considering entering

2) If #1 = yes, then let's see if it's attractive...can we win there? Is our product good/better than our competition's? Etc. If yes, let's definitely consider entering.

3) If #1 and #2 = yes, then, when we do enter, are we sure we can win? I.e. do we have the right plans. Will implementation actually pan out? Do we have the expertise, capital, etc.? In other words, if #2 is the thearectical, #3 is the reality.

Then, your summary becomes "I believe we should enter the market, if we can prove it's a good market, the it's attractive to us specifically, and that we will win it".

^Now this is a hypothesis :)

Read these 4 Q&As for some great context + discussion:

https://www.preplounge.com/en/consulting-forum/interviewer-led-case-interview-hyposthesis-and-ideas-7390

https://www.preplounge.com/en/consulting-forum/forming-a-hypothesis-case-in-point-vs-victor-cheng-7311

https://www.preplounge.com/en/consulting-forum/what-about-victor-chengs-loms-and-case-interview-secrets-book-are-outdated-9846

https://www.preplounge.com/en/consulting-forum/at-what-point-in-the-case-does-the-interviewee-state-hypothesis-9356

Hope this helps! This is a tricky topic that's difficult to properly answer in writting...if you want a more thorough explanation, and training in the mindset shift required here, don't hesitate to reach out!

Hypothesis driven effectively means having a answer-driven approach. When you do a profitability case, your hypothesis is that 'it could be a revenue or a cost problem - either the revenues are declining, or the costs are increasing'. When you further analyse revenues, your hypothesis is that 'Either my price is declining/stagnant, or i am unable to see sufficient volumes'.

You are effectively making a hypothesis to move forward in a case and drive the discusssion. Dont worry about it - i am fairly certain you have been doing this all along, while doing your case preparation. Feel free to reach out for further support.

Hi, I would only add this insightful answer to the discussion https://www.preplounge.com/en/consulting-forum/how-explicitly-do-you-need-to-state-your-hypothesis-in-mck-cases-1526

Best, Antonello

Being hypothesis driven is that, by leveraging data, you have an idea/some ideas of where the solution may be, and you do your questions, comments and inquiries in the case to try to proof that one valid/wrong.

However, carefull here, since this does not mean that you need to start every case with a "I believe the solution is this", since many times this is actually impossible to know until problem solving

Hope it helps!

Related Cases

MBB Final Round Case - Smart Education

McKinsey Digital / BCG Platinion: Oil & Gas Upstream Technology

Bain 1st round case – blissottica, bain + bcg - hot wheels with video solution, bcg - yodaphone, related case interview basics article(s), the role of the hypothesis in consulting.

Explore how hypotheses serve as guiding lights in the dynamic world of consulting, leading to innovative solutions and transformative outcomes.

What is Data-Driven PR, Part 4: Hypothesizing

At SHIFT, our approach is to apply equal parts art and science to build integrated programs that help brands connect with the people that matter most. But what does the 'science' part of communications entail? What does it look like in action? First and foremost, it means to be data-driven in our planning and execution; to make informed decisions based on data and research . In this series, we examine how to become a more data-driven communications professional.

Hypothesizing

In the previous step, we transformed our open-ended questions from Part 2 into variables and data sources. In this step, we will now construct the foundation of true data-driven PR: the hypothesis. Isn’t data the basis of data-driven PR and communications? Yes and no. What we call data-driven today was once called evidence-based decision-making. Data by itself isn’t necessarily helpful; data and insight that fuels decision-making is. To make the transition from raw data to decision-making fodder requires us to use data to prove or disprove something.In the scientific method, when we set out to prove or disprove something, we are constructing a hypothesis.

What is a hypothesis?

In data-driven PR, a hypothesis is a statement we intend to prove true or false through the use of the scientific method. Let’s return to our question from parts 2 and 3: What do customers like or dislike about the taste of espresso? How would we transform this open-ended question into a hypothesis? We would turn it from a question to a statement we will declare true or false.What do people associate with the taste of espresso? In a short, qualitative look at conversation about espresso, we find:

We find flavor profiles of strong, bitter, and burnt as most commonly associated with espresso. Thus, we could frame our hypothesis using any of those words:

Our customers like the burnt taste of espresso.
Our customers like the strong taste of espresso.
Our customers like the bitter taste of espresso.
Our customers dislike the burnt taste of espresso.
Our customers dislike the strong taste of espresso.
Our customers dislike the bitter taste of espresso.

Any one of those statements can be proven true or false, and is the foundation for our data-driven investigation.

What makes a bad hypothesis?

The construction of the hypothesis is the foundation of a data-driven investigation, which leads to data-driven PR done well. Thus, it’s important to understand ways in which a hypothesis can go bad.

Compound hypotheses

One of the most common ways a hypothesis goes bad is with compounding. Using the example above, this would be a bad hypothesis:

Our customers dislike the burnt, bitter taste of espresso.

The hypothesis above attempts to test two dimensions at the same time; from a logic perspective, either burnt or bitter could be true, or both could be true. There’s no way to isolate either one in the hypothesis. We should, as much as possible, test one thing at a time.

Malformed hypotheses

The other common way a hypothesis goes bad is if it’s formed incorrectly, such as leaving it a question, creating a statement that cannot be definitively proven true or false, or introducing new variables after the design of a question.

Maybe our customers enjoy the taste of burned coffee beans. (cannot be proven true or false)
Do our customers enjoy the taste of bitter coffee? (not a statement)
Our customers would like the taste of espresso with sugar. (adding in new variables not previously defined)

Any of the errors above would create, at best, inaccurate research, and at worst, a complete waste of time.

Next: testing

Now that we’ve established a hypothesis, our next step in the data-driven PR process is to test the hypothesis. We’ll examine ways to test and gather the data in order to prove our hypothesis true or false.

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Hypothesis-driven approach: the definitive guide
Using a hypothesis-driven approach is critical to solving a problem efficiently. In other words: A hypothesis will limit the number of analysis you need to perform to solve a problem. Thus, this is a way to apply the 80/20 principle and prioritize the issues (from your MECE issue tree) you want to investigate.
Hypothesis Driven Problem-Solving Explained: Tactics and Training
The four steps to hypothesis-driven problem solving are simple. In a nutshell: 1) Define the problem. The first step is to define the problem. This may seem like an obvious step, but it's important to be clear about what you're trying to solve. Sometimes people jump right into solving a problem without taking the time to fully understand it.
What I learned at McKinsey: How to be hypothesis-driven
McKinsey consultants follow three steps in this cycle: Form a hypothesis about the problem and determine the data needed to test the hypothesis. Gather and analyze the necessary data, comparing ...
Do's and Don'ts for Being Hypothesis-Driven at McKinsey
Being hypothesis-driven means developing an early perspective on the answer to a question, then using the hypothesis to inform what data is collected, analyses are performed, and insights are generated to support and/or revise the hypothesis. It is a living document and should be constantly tested and improved. There are also several important DO's and DON'Ts to keep in mind when being ...
How to Implement Hypothesis-Driven Development
The steps of the scientific method are to: Make observations. Formulate a hypothesis. Design an experiment to test the hypothesis. State the indicators to evaluate if the experiment has succeeded. Conduct the experiment. Evaluate the results of the experiment. Accept or reject the hypothesis.
What is hypothesis-driven development?
Hypothesis-driven development in a nutshell. As the name suggests, hypothesis-driven development is an approach that focuses development efforts around, you guessed it, hypotheses. To make this example more tangible, let's compare it to two other common development approaches: feature-driven and outcome-driven.
What Is A Hypothesis?
Hypothesis Definition. In the context of a consulting interview, a hypothesis definition is "a testable statement that needs further data for verification". In other words, the meaning of a hypothesis is that it's an educated guess that you think could be the answer to your client's problem. A hypothesis is therefore not always true.
Hypothesis-Driven Development (Practitioner's Guide)
Like agile, hypothesis-driven development (HDD) is more a point of view with various associated practices than it is a single, particular practice or process. ... Now, that doesn't mean you need to put on a lab coat and be super formal about what you do. What it does mean is that as we deal with uncertainty (aka innovate), ...
Case Interview 101: What Is A Hypothesis Driven Approach?
Being efficient at problem solving boils down to designing a good analytical approach. Photo by Volodymyr Hryshchenko on Unsplash. This is where "hypothesis-driven approach" comes in. Being hypothesis-driven in problem solving means working on trying to debunk (or prove) each component of the problem at a time, starting with components that ...
How to Implement Hypothesis-Driven Development
Make observations. Formulate a hypothesis. Design an experiment to test the hypothesis. State the indicators to evaluate if the experiment has succeeded. Conduct the experiment. Evaluate the results of the experiment. Accept or reject the hypothesis. If necessary, make and test a new hypothesis.
PDF Hypothesis-Driven Research
Hypothesis-driven study is a fundamental, useful skill; applicable to many other things Be aware of applying hypothesis-driven research Limitation of hypothesis-driven research Design your research before doing it: Require a lot of thinking, get input from others Integrate hypothesis-driven and discovery-driven research Summary
Data-driven hypothesis development
Data-driven hypothesis development (DDHD) is an effective approach when facing complex "known unknown" and "unknown unknown" problems. There are four steps to the approach: 1. Define the goal using data. Problem solving starts with well defined problems, however, we know that "known unknowns" and "unknown unknowns" are rarely ...
Use a Hypothesis-Driven Approach To Solve Complex Problems
A hypothesis-driven approach is the secret to solving any complex business problem — even the most convoluted and confusing ones. If you run a business, you're going to face complicated scenarios. ... In this article, we'll explore what it means to be hypothesis-driven in your business. Then, I'll outline a five-step process to problem ...
Lessons from Hypothesis-Driven Development
The principle of hypothesis-driven development is to apply scientific methods to product development. Defining success criteria and then forming testable hypotheses around how to meet them. Over ...
5 steps to a hypothesis-driven design process
Recruit the users you want to target, have a time frame, and put the design in front of the users. 5. Learn and build. You just learned that the result was positive and you're excited to roll out the feature. That's great! If the hypothesis failed, don't worry—you'll be able to gain some insights from that experiment.
The 6 Steps that We Use for Hypothesis-Driven Development
Hypothesis-driven development is a prototype methodology that allows product designers to develop, test, and rebuild a product until it's acceptable by the users. It is an iterative measure that explores assumptions defined during the project and attempts to validate it with users' feedbacks.
Hypothesis-driven development: Definition, why and implementation
Hypothesis-driven development emphasizes a data-driven and iterative approach to product development, allowing teams to make more informed decisions, validate assumptions, and ultimately deliver products that better meet user needs. Hypothesis-driven development (HDD) is an approach used in software development and product management.
Guide for Hypothesis-Driven Development: How to Form a List of
A hypothesis-driven approach provides a structured way of consolidating ideas and building hypotheses based on objective criteria. This methodology provides a deep understanding of prioritizing features related to business goals and desired user outcomes. How to Test the Hypothesis of Product Demand and Value Without Development
Hypothesis-driven Research
The scope of a well-designed methodology in a hypothesis-driven research equips the researcher to establish an opportunity to state the outcome of the study. A provisional statement in which the relationship between two variables is described is known as hypothesis. It is very specific and offers the freedom of evaluating a prediction between ...
Using a Hypothesis-Driven Approach in Analyzing (and Making Sense) of
In research and data analysis, a hypothesis-driven approach is one of the main methods for using data to test and, ultimately, prove (or disprove) assertions. To do that, researchers collect a sufficient amount of data on the subject and then approach it with a specific hypothesis in mind.
Hypothesis Requirements
5. It DOES NOT misdirect to the researcher. The hypothesis should not address your capabilities. "Discovering the mechanism behind X will enable us to better detect the pathogen." This example tests the ability of the researchers to take information and use it; this is a result of successful hypothesis-driven research, not a testable ...
How to be "hypothesis driven"? What does is really mean?
Hypothesis driven effectively means having a answer-driven approach. When you do a profitability case, your hypothesis is that 'it could be a revenue or a cost problem - either the revenues are declining, or the costs are increasing'. When you further analyse revenues, your hypothesis is that 'Either my price is declining/stagnant, or i am ...
What is Data-Driven PR, Part 4: Hypothesizing
The construction of the hypothesis is the foundation of a data-driven investigation, which leads to data-driven PR done well. Thus, it's important to understand ways in which a hypothesis can go bad. Compound hypotheses. One of the most common ways a hypothesis goes bad is with compounding. Using the example above, this would be a bad hypothesis:
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Hypothesis-driven approach: the definitive guide

Table of Contents

The right hypothesis-driven approach in case interviews

Why you must (always) use a hypothesis in your case interviews

A hypothesis helps you focus on what’s important to solve the case

Get 4 Complete Case Interview Courses For Free

A hypothesis tells your interviewers why you want to do an analysis

How to use a hypothesis-driven approach in your case interviews?

The quantitative approach: use the available data

The qualitative approach: use your business acumen

Want to learn more?

Form a hypothesis in these two critical moments of your case interviews

After an analysis

Hypothesis-driven approach in case interviews: a conclusion

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