hypothesis driven development example

How to Implement Hypothesis-Driven Development

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  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 behaviour 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 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 biased interpretations of the 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 know we have succeeded 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 statistically 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 Know We Have Succeeded 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 behaviour. Alternative testings options can be customer surveys, paper prototypes, user and/or guerrilla 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 cost, 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.

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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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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.

Copyright © 2022 Alex Cowan · All rights reserved.

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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:

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:

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:

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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6 Steps Of Hypothesis-Driven Development That Works

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.

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How to apply hypothesis-driven development, the statsig team.

Ever wondered how to streamline your software development process to align more closely with actual user needs and business goals?

Hypothesis-Driven Development (HDD) could be the answer, blending the rigor of the scientific method with the creativity of engineering. This approach not only accelerates development but also enhances the precision and relevance of the features you deploy.

HDD isn't just a fancy term, it's a structured methodology that transforms guessing in product development into an evidence-based strategy. By focusing on hypotheses, you can make clearer decisions and avoid the common pitfalls of assumption-based approaches.

Here's how you can apply this method to boost your team's efficiency and product success.

Introduction to hypothesis-driven development

Hypothesis-Driven Development (HDD) applies the scientific method to software engineering, fostering a culture of experimentation and learning. Essentially, it involves forming a hypothesis about a feature's impact, testing it in a real-world scenario, and using the results to guide further development. This method helps teams move from "we think" to "we know," ensuring that every feature adds real value to the product.

Benefits of HDD include:

Improved accuracy: By testing assumptions, you ensure that only the features that truly meet user needs and drive business goals make it to production.

Enhanced team agility: HDD allows teams to adapt quickly based on empirical data, making it easier to pivot or iterate on features.

Adopting HDD means shifting from a feature-focused to a results-focused mindset, a change that can significantly enhance both the development process and the end product. By integrating hypothesis testing into your workflow, you not only build better software but also foster a more knowledgeable and agile development team.

Setting the stage for HDD

Defining clear, testable hypotheses before starting the development process is crucial. This ensures that every feature developed serves a specific, measurable goal. Remember, a well-defined hypothesis sets the stage for meaningful experimentation and impactful results.

User feedback and data analysis play pivotal roles in shaping these hypotheses. You gather insights directly from your users and analyze existing data to hypothesize what changes might improve your product. This approach ensures that your development efforts align closely with user needs and expectations.

For example, feature flagging allows you to test hypotheses in production environments without disrupting the user experience. This method provides real-time feedback and data to refine your hypotheses further.

Designing effective experiments

Selecting relevant metrics and establishing control groups are key components in designing experiments. You need metrics that directly reflect the changes hypothesized. Establishing a control group ensures that any observed changes are due to the modification and not external variables.

Utilizing tools like feature flags ensures that your experiments are both scalable and repeatable. Feature flags allow you to manage who sees what feature and when, making it easier to roll out changes incrementally. This approach minimizes risk and provides flexibility in testing.

Techniques for scalability and repeatability :

Use feature flags to segment user groups and roll out changes selectively.

Ensure data consistency across tests by using standardized data collection methods.

Automate the deployment and rollback processes to react quickly to experiment results.

By following these strategies, you can ensure that your hypothesis-driven experiments yield valuable insights and drive product improvements effectively.

Implementing experimentation at scale

Tools and platforms like Statsig enhance hypothesis-driven development by enabling feature flagging and experimentation. These tools integrate into your development workflows seamlessly. They provide a robust framework for managing experiments without disrupting existing processes.

Seamless integration into development workflows involves several steps:

Automate the setup process : Tools should easily integrate with your CI/CD pipelines.

Use APIs for customization : Flexible APIs allow you to tailor experiments to your specific needs ( learn more about API integration ).

Leverage dashboard features : Platforms offer dashboards for real-time results monitoring, which assists in quick decision-making.

By adopting these tools, you ensure that experimentation scales with your application's growth and complexity. This approach supports continuous improvement and helps you make data-driven decisions efficiently.

Analyzing experiment results

Analyzing data post-experiment is crucial to determining the success or failure of your hypothesis. You begin by gathering and segmenting the data collected during the experiment phase. Use statistical tools to analyze these data sets for patterns or significant outcomes.

Understanding statistical significance plays a pivotal role in hypothesis-driven development (HDD). This involves determining whether the results observed are due to the changes made or random variations:

Perform a t-test or use a p-value to assess the significance.

Ensure the sample size is adequate to justify the results.

These methods guide your decision-making process, indicating whether to adopt, iterate, or discard the tested hypothesis. Effective analysis not only confirms the validity of your hypothesis but also enhances the reliability of your development process.

Learning from success and failure

Documenting outcomes is essential, whether your experiments succeed or fail. Start by creating a structured template that captures key metrics, observations, and the conditions under which the experiment ran. This practice ensures that you maintain a historical data repository which can guide future hypotheses and prevent repetitive failures.

Learning from both success and failure sharpens your hypothesis-driven development skills. For successes, document what worked and why, linking outcomes to specific actions or changes. For failures, identify missteps and misunderstood variables to refine future experiments. This continuous documentation feeds into a knowledge base that becomes a valuable resource for your team.

Iterating and integrating feedback enhance product development progressively. Incorporate lessons from each experiment into the next cycle of hypothesis formulation and testing. This approach, highlighted in discussions about good engineering culture , fosters a dynamic environment where improvements are continual and responsive to user feedback.

By embracing these practices, you ensure that your development process remains agile, informed, and increasingly effective over time.

Closing thoughts

Hypothesis-Driven Development offers a powerful framework for aligning software development with user needs and business objectives. By embracing experimentation, data-driven decision making, and continuous learning, teams can create products that truly resonate with their target audience.

While adopting HDD requires a shift in mindset and the right tools, the benefits it brings in terms of improved accuracy, agility, and user satisfaction make it a worthwhile investment for any software development organization.

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Hypothesis Driven Development

The scientific method.

Breaking it down

Defining the right problem.

The persona hypothesis

The JTBD hypothesis

The demand hypothesis

Finding the right solution.

The usability hypothesis

Divergence & convergence.

Testing hypotheses

Testing the problem, testing the solution.

• Cowan, A. (n.d.). Hypothesis Driven Development: Practitioners Guide. Available here
• Cowan, A. (2023). Hypothesis Driven Development: A Guide to Smarter Product Management . 2nd ed. Charlottesville: Cooke & McDouglas

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Copyright © 2024 Michael Alexander Delmar

Hypothesis-Driven Development

Hypothesis-Driven Development (HDD) is a software development approach rooted in the philosophy of systematically formulating and testing hypotheses to drive decision-making and improvements in a product or system. At its core, HDD seeks to align development efforts with the goal of discovering what resonates with users. This philosophy recognizes that assumptions about user behavior and preferences can often be flawed, and the best way to understand users is through experimentation and empirical evidence.

In the context of HDD, features and user stories are often framed as hypotheses. This means that instead of assuming a particular feature or enhancement will automatically improve the user experience, development teams express these elements as testable statements. For example, a hypothesis might propose that introducing a real-time chat feature will lead to increased user engagement by facilitating instant communication.

The Process

The process of Hypothesis-Driven Development involves a series of steps. Initially, development teams formulate clear and specific hypotheses based on the goals of the project and the anticipated impact on users. These hypotheses are not merely speculative ideas but are designed to be testable through concrete experiments.

Once hypotheses are established, the next step is to design and implement experiments within the software. This could involve introducing new features, modifying existing ones, or making adjustments to the user interface. Throughout this process, the emphasis is on collecting relevant data that can objectively measure the impact of the changes being tested.

Validating Hypotheses

The collected data is then rigorously analyzed to determine the validity of the hypotheses. This analytical phase is critical for extracting actionable insights and understanding how users respond to the implemented changes. If a hypothesis is validated, the development team considers how to build upon the success. Conversely, if a hypothesis is invalidated, adjustments are made based on the lessons learned from the experiment.

HDD embraces a cycle of continuous improvement. As new insights are gained and user preferences evolve, the development process remains flexible and adaptive. This iterative approach allows teams to respond to changing conditions and ensures that the software is consistently refined in ways that genuinely resonate with users. In essence, Hypothesis-Driven Development serves as a methodology that not only recognizes the complexity of user behavior but actively seeks to uncover what truly works through a structured and empirical approach.

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Understanding Hypothesis-Driven Development in Software Development

• February 9, 2024

In software development, there are various approaches and methodologies that developers employ to ensure the successful delivery of high-quality products. One such approach that has gained significant traction in recent years is Hypothesis-Driven Development (HDD). HDD is a mindset that drives the development process by formulating and validating hypotheses to guide decision-making at each stage of development.

The Concept of Hypothesis-Driven Development

Hypothesis-Driven Development (HDD) is a systematic and iterative approach that leverages the scientific method to inform software development decisions. It is based on the premise that by formulating hypotheses and conducting experiments, developers can gather empirical evidence and make informed choices during the development process . The essence of HDD lies in embracing uncertainty and treating software development as a learning process .

Defining Hypothesis-Driven Development

At its core, HDD involves framing hypotheses about user behavior , product features, or system performance and then designing experiments to test these hypotheses. These experiments can take the form of A/B tests, user feedback sessions, or performance benchmarks. For example, let’s say a development team wants to improve the user interface of their application. They might hypothesize that by simplifying the navigation menu, users will find it easier to navigate through the app. To test this hypothesis, they could conduct A/B tests where one group of users sees the original menu and another group sees the simplified menu. By analyzing the data collected from these experiments, the team can make data-driven decisions and improve the overall quality of the product.

Furthermore, HDD encourages developers to iterate and refine their hypotheses based on the experiment results. This iterative process allows for continuous learning and improvement . For instance, if the A/B test results show that the simplified navigation menu did not lead to a significant improvement in user experience, the team can go back to the drawing board and formulate new hypotheses to test. This flexibility and adaptability are key aspects of HDD that enable developers to respond to changing user needs and market demands.

The Importance of Hypothesis-Driven Development

The significance of HDD lies in its ability to mitigate assumptions and biases that can often creep into the development process. Assumptions and biases can lead to misguided decisions and wasted resources. By relying on empirical evidence, developers can make better-informed decisions and ensure that their efforts are aligned with user needs and expectations. HDD provides a structured framework for gathering and analyzing data, allowing developers to make evidence-based choices rather than relying solely on intuition or personal opinions.

Moreover, HDD fosters a culture of continuous improvement within development teams. It encourages teams to learn from their failures and adapt their hypotheses accordingly. Instead of viewing failures as setbacks, HDD treats them as valuable learning opportunities. By embracing failure as a stepping stone to success, teams can identify areas for improvement and make adjustments to their hypotheses and experiments. This iterative process not only enhances the quality of the software being developed but also promotes a growth mindset among team members.

In conclusion, Hypothesis-Driven Development is a powerful approach that brings the scientific method into software development. By formulating hypotheses, conducting experiments, and analyzing data , developers can make informed decisions, mitigate assumptions and biases, and foster a culture of continuous improvement. Embracing HDD allows teams to create software that is truly aligned with user needs and expectations, ultimately leading to a better user experience and increased success in the market.

The Process of Hypothesis-Driven Development

The HDD process can be broken down into several key steps that provide a structured framework for developers to follow:

Identifying the Hypothesis

The first step in HDD involves formulating clear and testable hypotheses that address specific areas of uncertainty or improvement in the product. These hypotheses can range from user experience enhancements to performance optimizations. For example, a hypothesis could be that improving the loading time of a website will lead to a decrease in bounce rate and an increase in user engagement. The key is to ensure that the hypotheses are measurable and actionable.

Developers may gather insights from user feedback, market research, or data analysis to identify areas that need improvement. By understanding the pain points and challenges faced by users, developers can formulate hypotheses that directly address these issues.

Designing the Experiment

Once the hypotheses are defined, the next step is to design experiments that will validate or invalidate the hypotheses. These experiments should be well-planned and controlled to ensure accurate results. Collaborating with cross-functional teams, such as designers and product managers, can help in designing comprehensive experiments.

For instance, in the example of improving website loading time, the experiment could involve creating two versions of the website – one with the optimized loading time and another with the current loading time. Randomly assigning users to each version and measuring metrics such as bounce rate, page views, and conversion rate can help determine the impact of the loading time improvement.

Implementing the Experiment

After the experiment design is finalized, the actual implementation takes place. This may involve making changes to the software, setting up the necessary data tracking systems, or conducting user tests. It is essential to meticulously follow the experimental design to ensure accurate data collection.

In the case of improving website loading time, developers may need to optimize code, compress images, or leverage caching techniques to achieve the desired improvement. They may also need to set up analytics tools to track user behavior and gather relevant data for analysis.

Analyzing the Results

Once the experiment has been executed, the data collected needs to be analyzed. This analysis aims to draw conclusions about the validity of the hypotheses and the impact of the changes made. It is important to use statistical methods to ensure the reliability of the results.

For example, statistical tests such as t-tests or chi-square tests can be used to determine if the observed differences in metrics between the two versions of the website are statistically significant. This analysis helps developers make informed decisions about the effectiveness of their hypotheses and the potential impact on the product.

By following this iterative process, developers can constantly refine their hypotheses and adjust their development strategies based on the emerging insights. This ensures that the final product aligns with user expectations and provides a seamless experience. Continuous experimentation and data-driven decision-making are at the core of hypothesis-driven development, enabling developers to create products that truly meet the needs of their users.

Benefits of Hypothesis-Driven Development

HDD offers numerous benefits that contribute to the success of software development projects:

Enhancing Product Quality

By adopting a hypothesis-driven approach, developers can make evidence-based decisions that improve the overall quality of the product. Through continuous experimentation and feedback, the product can be refined to meet the specific needs and desires of the users.

For example, let’s say a development team is working on a mobile app that offers a personalized shopping experience. By using HDD, they can hypothesize that adding a recommendation engine based on user preferences will enhance the product’s quality. They can then test this hypothesis by implementing the feature and gathering feedback from a group of users. If the results show that the recommendation engine indeed improves the user experience and leads to more purchases, the team can confidently integrate it into the final product.

Reducing Development Time

HDD helps in reducing development time by enabling developers to focus their efforts on features and enhancements that are proven to provide value to users. By avoiding unnecessary guesswork and speculation, developers can streamline the development process and deliver products faster.

Consider a scenario where a software development team is tasked with creating a project management tool. Instead of spending months building all possible features, they can use HDD to prioritize the most critical functionalities based on user needs. They can formulate hypotheses about which features will have the most significant impact on productivity and test them through iterative development cycles. This approach allows the team to release a minimum viable product quickly and gather real-world feedback, which can then inform further development and reduce time wasted on unnecessary features.

Improving Team Collaboration

Collaboration is a key aspect of HDD, as it involves cross-functional teams working together to design experiments, analyze results, and make informed decisions. This collaboration fosters a sense of shared ownership and drives innovation, as different perspectives and expertise contribute to the development process.

Imagine a development team where designers, developers, and product managers work in silos, rarely communicating or sharing ideas. By implementing HDD, these teams can come together to formulate hypotheses and design experiments that address user pain points. Through collaborative analysis of results, they can gain a deeper understanding of user needs and preferences, leading to more innovative solutions. This shared ownership and collaborative spirit not only improves the development process but also creates a positive work environment where everyone feels valued and empowered.

Challenges in Hypothesis-Driven Development

While Hypothesis-Driven Development (HDD) offers significant advantages, it is not without its challenges. Awareness of these challenges can help development teams proactively tackle them, ensuring a smoother implementation and maximizing the benefits of HDD.

Potential Risks and How to Mitigate Them

Implementing HDD requires careful consideration of potential risks to ensure accurate and reliable results. One of the key risks is data integrity issues, which can arise from incomplete or inaccurate data collection. To mitigate this risk, development teams should establish robust data collection methodologies, including clear guidelines for data entry and validation processes. Regular data audits and quality checks can help maintain the integrity of the collected data.

Another risk associated with HDD is variable user behavior. Users may exhibit different preferences, habits, or responses to the changes introduced through HDD. To address this, development teams should conduct controlled experiments, carefully selecting a diverse range of users to participate in testing. By including users with different backgrounds, demographics, and usage patterns, teams can gather a more comprehensive understanding of how their product or feature performs across various user segments.

Lastly, premature conclusions can pose a risk to the effectiveness of HDD. It is crucial to avoid drawing hasty conclusions based on limited data or early results. Instead, development teams should adopt a data-driven approach, collecting sufficient data and conducting thorough analysis before making any final judgments. This may involve setting clear success metrics and monitoring them over an extended period to ensure accurate evaluation.

Overcoming Resistance to Change

Adopting HDD may face resistance from team members who are more accustomed to traditional development approaches. It is natural for individuals to be hesitant about embracing new methodologies, especially if they have been successful with their existing practices. To overcome this resistance, effective communication and education are essential.

Development teams should clearly articulate the benefits of HDD, emphasizing how it can lead to faster iterations, improved product quality, and increased customer satisfaction. By highlighting the positive outcomes that HDD can bring, team members are more likely to understand and appreciate the value of this approach.

In addition to communication, providing support and training to team members during the transition can also help alleviate resistance. Offering workshops, seminars, or one-on-one coaching sessions can equip team members with the necessary skills and knowledge to effectively apply HDD in their work. This proactive approach ensures that everyone is on the same page and empowers team members to embrace the change with confidence.

Future of Hypothesis-Driven Development

The future of Hypothesis-Driven Development (HDD) looks promising, as it aligns well with the growing emphasis on agile practices and data-driven decision-making in the software development community. However, the potential of HDD goes beyond its current state, with several trends shaping its future and the role it plays in agile practices.

Trends Shaping Hypothesis-Driven Development

One of the notable trends shaping HDD is the increasing availability of data collection and analysis tools. These tools streamline the experimentation process and provide actionable insights, making it easier for development teams to adopt HDD principles. With the advancements in data analytics, teams can now gather and analyze vast amounts of data, enabling them to make more informed decisions based on real-time user feedback.

Furthermore, the integration of machine learning and artificial intelligence techniques presents exciting possibilities for further enhancing HDD methodologies. By leveraging these technologies, development teams can automate the hypothesis generation process, allowing for faster iterations and more accurate predictions. This integration also enables the identification of patterns and correlations in data that might be missed by human analysts, leading to more robust and reliable hypotheses.

The Role of Hypothesis-Driven Development in Agile Practices

HDD complements agile practices by providing a structured and iterative approach to product development. It aligns well with the agile principle of delivering working software incrementally and responding to change. By incorporating HDD into their agile workflows, development teams can effectively balance the need for rapid iterations with the importance of data-driven decision-making.

Moreover, HDD promotes collaboration and cross-functional teamwork within development teams. By encouraging the formulation and testing of hypotheses, HDD fosters a culture of shared learning and continuous improvement. It encourages team members to challenge assumptions, share insights, and work together towards a common goal of delivering high-quality software that meets user needs and expectations.

In conclusion, understanding and embracing Hypothesis-Driven Development is crucial for software development teams seeking to deliver high-quality products that meet user needs and expectations. By adopting a scientific approach and constantly testing and refining hypotheses, developers can make informed decisions, reduce development time, and enhance collaboration. As the future of software development continues to evolve, HDD will play a vital role in ensuring the success of development projects. The trends shaping HDD, such as the increasing availability of data collection and analysis tools and the integration of machine learning and artificial intelligence, will further enhance its effectiveness and enable teams to deliver even more innovative and user-centric solutions.

Take Your Team’s Collaboration to the Next Level with Teamhub

As you explore the power of Hypothesis-Driven Development to enhance your software projects, consider the role a robust collaboration platform like Teamhub can play in streamlining your processes. Teamhub is designed to bring small teams together, offering a centralized hub that integrates Projects and Documentation seamlessly. Embrace our vision of a single hub for your entire team and join the thousands of companies boosting productivity with Teamhub. Ready to transform your team’s collaboration and productivity? Start your free trial today and experience the difference.

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Scrum and Hypothesis Driven Development

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Scrum was built to better manage risk and deliver value by focusing on inspection and encouraging adaptation. It uses an empirical approach combined with self organizing, empowered teams to effectively work on complex problems. And after reading Jeff Gothelf ’s and Josh Seiden ’s book “ Sense and Respond: How Successful Organizations Listen to Customers and Create New Products Continuously ”, I realized that the world is full of complex problems. This got me thinking about the relationship between Scrum and modern organizations as they pivot toward becoming able to ‘sense and respond’. So, I decided to ask Jeff Gothelf… Here is a condensed version of our conversation.

Sense & Respond was exactly this attempt to change the hearts and minds of managers, executives and aspiring managers. It makes the case that first and foremost, any business of scale or that seeks to scale is in the software business. We share a series of compelling case studies to illustrate how this is true across nearly every industry. We then move on to the second half of the book where we discuss how managing a software-based business is different. We cover culture, process, staffing, planning, budgeting and incentives. Change has to be holistic.

What you are describing is the challenge of ownership. Product Owner (PO) is the role in the Scrum Framework empowered to make decisions about what and when things are in the product. But disempowerment is a real problem in most organizations, with their POs not having the power to make decisions. Is this something you see when introducing the ideas of Sense and Respond?

There will always be situations where things simply have to get built. Legal and compliance are two great examples of this. In these, low risk, low uncertainty situations a more straightforward execution is usually warranted. That said, just because a feature has to be included for compliance reasons doesn’t mean there is only one way to implement it. What teams will often find is that there is actual flexibility in how these (actual) requirements can be implemented with some being more successful and less distracting to the overall user experience than others. The level of discovery that you would expend on these features is admittedly smaller but it shouldn’t be thrown out altogether as these features still need to figure into a holistic workflow.   

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Why hypothesis-driven development is key to DevOps

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The definition of DevOps, offered by  Donovan Brown is  "The union of people , process , and products to enable continuous delivery of value to our customers. " It accentuates the importance of continuous delivery of value. Let's discuss how experimentation is at the heart of modern development practices.

Reflecting on the past

Before we get into hypothesis-driven development, let's quickly review how we deliver value using waterfall, agile, deployment rings, and feature flags.

In the days of waterfall , we had predictable and process-driven delivery. However, we only delivered value towards the end of the development lifecycle, often failing late as the solution drifted from the original requirements, or our killer features were outdated by the time we finally shipped.

Here, we have one release X and eight features, which are all deployed and exposed to the patiently waiting user. We are continuously delivering value—but with a typical release cadence of six months to two years, the value of the features declines as the world continues to move on . It worked well enough when there was time to plan and a lower expectation to react to more immediate needs.

The introduction of agile allowed us to create and respond to change so we could continuously deliver working software, sense, learn, and respond.

Now, we have three releases: X.1, X.2, and X.3. After the X.1 release, we improved feature 3 based on feedback and re-deployed it in release X.3. This is a simple example of delivering features more often, focused on working software, and responding to user feedback. We are on the path of continuous delivery, focused on our key stakeholders: our users.

Using deployment rings and/or feature flags , we can decouple release deployment and feature exposure, down to the individual user, to control the exposure—the blast radius—of features. We can conduct experiments; progressively expose, test, enable, and hide features; fine-tune releases, and continuously pivot on learnings and feedback.

When we add feature flags to the previous workflow, we can toggle features to be ON (enabled and exposed) or OFF (hidden).

Here, feature flags for features 2, 4, and 8 are OFF, which results in the user being exposed to fewer of the features. All features have been deployed but are not exposed (yet). We can fine-tune the features (value) of each release after deploying to production.

Ring-based deployment limits the impact (blast) on users while we gradually deploy and evaluate one or more features through observation. Rings allow us to deploy features progressively and have multiple releases (v1, v1.1, and v1.2) running in parallel.

Exposing features in the canary and early-adopter rings enables us to evaluate features without the risk of an all-or-nothing big-bang deployment.

Feature flags decouple release deployment and feature exposure. You "flip the flag" to expose a new feature, perform an emergency rollback by resetting the flag, use rules to hide features, and allow users to toggle preview features.

When you combine deployment rings and feature flags, you can progressively deploy a release through rings and use feature flags to fine-tune the deployed release.

See deploying new releases: Feature flags or rings , what's the cost of feature flags , and breaking down walls between people, process, and products for discussions on feature flags, deployment rings, and related topics.

Adding hypothesis-driven development to the mix

Hypothesis-driven development is based on a series of experiments to validate or disprove a hypothesis in a complex problem domain where we have unknown-unknowns. We want to find viable ideas or fail fast. Instead of developing a monolithic solution and performing a big-bang release, we iterate through hypotheses, evaluating how features perform and, most importantly, how and if customers use them.

Template: We believe {customer/business segment} wants {product/feature/service} because {value proposition}. Example: We believe that users want to be able to select different themes because it will result in improved user satisfaction. We expect 50% or more users to select a non-default theme and to see a 5% increase in user engagement.

Every experiment must be based on a hypothesis, have a measurable conclusion, and contribute to feature and overall product learning. For each experiment, consider these steps:

• Observe your user
• Define a hypothesis and an experiment to assess the hypothesis
• Define clear success criteria (e.g., a 5% increase in user engagement)
• Run the experiment
• Evaluate the results and either accept or reject the hypothesis

Let's have another look at our sample release with eight hypothetical features.

When we deploy each feature, we can observe user behavior and feedback, and prove or disprove the hypothesis that motivated the deployment. As you can see, the experiment fails for features 2 and 6, allowing us to fail-fast and remove them from the solution. We do not want to carry waste that is not delivering value or delighting our users! The experiment for feature 3 is inconclusive, so we adapt the feature, repeat the experiment, and perform A/B testing in Release X.2. Based on observations, we identify the variant feature 3.2 as the winner and re-deploy in release X.3. We only expose the features that passed the experiment and satisfy the users.

Hypothesis-driven development lights up progressive exposure

When we combine hypothesis-driven development with progressive exposure strategies, we can vertically slice our solution, incrementally delivering on our long-term vision. With each slice, we progressively expose experiments, enable features that delight our users and hide those that did not make the cut.

But there is more. When we embrace hypothesis-driven development, we can learn how technology works together, or not, and what our customers need and want. We also complement the test-driven development (TDD) principle. TDD encourages us to write the test first (hypothesis), then confirm our features are correct (experiment), and succeed or fail the test (evaluate). It is all about quality and delighting our users , as outlined in principles 1, 3, and 7  of the Agile Manifesto :

• Our highest priority is to satisfy the customers through early and continuous delivery of value.
• Deliver software often, from a couple of weeks to a couple of months, with a preference to the shorter timescale.
• Working software is the primary measure of progress.

More importantly, we introduce a new mindset that breaks down the walls between development, business, and operations to view, design, develop, deliver, and observe our solution in an iterative series of experiments, adopting features based on scientific analysis, user behavior, and feedback in production. We can evolve our solutions in thin slices through observation and learning in production, a luxury that other engineering disciplines, such as aerospace or civil engineering, can only dream of.

The good news is that hypothesis-driven development supports the empirical process theory and its three pillars: Transparency , Inspection , and Adaption .

But there is more. Based on lean principles, we must pivot or persevere after we measure and inspect the feedback. Using feature toggles in conjunction with hypothesis-driven development, we get the best of both worlds, as well as the ability to use A|B testing to make decisions on feedback, such as likes/dislikes and value/waste.

Hypothesis-driven development:

• Is about a series of experiments to confirm or disprove a hypothesis. Identify value!
• Delivers a measurable conclusion and enables continued learning.
• Enables continuous feedback from the key stakeholder—the user—to understand the unknown-unknowns!
• Enables us to understand the evolving landscape into which we progressively expose value.

Progressive exposure:

• Is not an excuse to hide non-production-ready code. Always ship quality!
• Is about deploying a release of features through rings in production. Limit blast radius!
• Is about enabling or disabling features in production. Fine-tune release values!
• Relies on circuit breakers to protect the infrastructure from implications of progressive exposure. Observe, sense, act!

What have you learned about progressive exposure strategies and hypothesis-driven development? We look forward to your candid feedback.

Comments are closed.

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What is Hypothesis-Driven Development?

• June 17, 2022

Understanding the power of Hypothesis-Driven Development with Interaction Labs

In the fast-paced world of Silicon Valley, where innovation is the currency and disruption is the norm, companies are constantly searching for new ways to stay ahead of the curve.

Amidst this ever-changing landscape, one approach has emerged as a guiding principle for product development : hypothesis-driven development. This methodology, rooted in the scientific method, emphasizes the formulation and testing of hypotheses as a cornerstone of the development process. Let’s delve deeper into what hypothesis-driven development entails, its benefits, and some real-world examples of its application.

Understanding Hypothesis-Driven Product Development

At its core, hypothesis-driven development is about making informed guesses, testing them rigorously, and learning from the results. It begins with identifying a problem or opportunity in the market. Once the problem is defined, the development team formulates hypotheses about potential solutions or approaches. These hypotheses serve as guiding principles throughout the development process, shaping decisions about product features, design, and functionality.

The Power of Experimentation

Key to hypothesis-driven product development is the concept of experimentation. Instead of relying solely on intuition or market research, teams design experiments to test their hypotheses in real-world scenarios. These experiments take various forms, from A/B testing different features to launching MVPs (Minimum Viable Products) to gauge user interest and feedback.

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Case Study: Airbnb

One of the most celebrated examples of hypothesis-driven development comes from Airbnb, the online marketplace for lodging and travel experiences. In the early days of the company, the founders had a hypothesis: that people would be willing to rent out their homes to travelers, providing a more authentic and affordable alternative to traditional hotels.

To test this hypothesis, Airbnb launched a simple website allowing hosts to list their properties and travellers to book them. Initially, the founders took professional photographs of the listings themselves, hypothesising that high-quality images would increase booking rates. When they saw a significant uptick in bookings after implementing this change, their hypothesis was validated.

While Airbnb is data driven, they don’t let data push them around. Instead of developing reactively to metrics, the team often starts with a creative hypothesis, implements a change, reviews how it impacts the business and then repeats that process.

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Iterative Learning and Adaptation

A core tenet of hypothesis-driven development is iteration. Based on the results of experiments, teams iterate on their products, making improvements and refinements along the way. This iterative process allows companies to adapt to changing market dynamics and user feedback, ensuring that their products remain relevant and competitive.

Case Study: Spotify

Spotify, the popular music streaming service, is another example of hypothesis-driven development in action. When Spotify first entered the market, it faced stiff competition from established players like iTunes and Pandora. However, the company had a hypothesis: that users would be willing to pay for a subscription service that offered unlimited access to a vast library of music, supported by targeted advertising.

Through a series of experiments, including offering free trials and refining its recommendation algorithms, Spotify was able to validate its hypothesis and attract millions of paying subscribers worldwide. By continuously iterating on its product based on user feedback and market insights, Spotify has remained at the forefront of the music streaming industry.

Key Takeaways :

Hypothesis-driven development has emerged as a powerful framework for driving innovation and growth. By formulating hypotheses, designing experiments, and iterating based on results, companies can unlock new opportunities, mitigate risks, and stay ahead of the competition. This data-driven approach not only helps companies build products that resonate with users but also fosters a culture of experimentation and continuous improvement. As the tech landscape continues to evolve, hypothesis-driven development will remain a cornerstone of successful product development strategies, enabling companies to innovate and thrive in today’s dynamic marketplace.

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Hypothesis-driven Development

.css-3n7dj1{box-sizing:border-box;margin:0;min-width:0;display:block;color:var(--theme-ui-colors-heading,#edf2f7);font-weight:bold;-webkit-text-decoration:none;text-decoration:none;margin-bottom:1rem;font-size:1.5rem;position:relative;} Definition of Hypothesis-driven Development

Hypothesis-driven Development is a development approach where features are treated as hypotheses, aiming to validate assumptions.

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Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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What is Hypothesis Driven Research?

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PLEASE ANSWER ASAP!!!! Video: Small Rockets Are the Next Space Revolution 1. What are the three things that you must do if you want to democratize space? Briefly explain each of these things. 2. How often can this private company launch a rocket in New Zealand? Why could it be difficult to launch this often? 3. How does this company manufacture its rocket engines? How fast can they make these? 4. What causes the most “space junk”? How does the Electron rocket help fix this problem? Video: Sunita Williams of NASA Provides a Tour of the ISS Orbital Laboratory 1. How do astronauts sleep in zero gravity? Where are the sleep stations located? 2. How have the spacesuits changed since first designed in 1997? 3. What continent was the space station viewing? How did Sunny know this? 4. How do spacecraft attach to the space station? 5. Do you think Sunny should have put her hair in a ponytail? Why or why not?

To democratize space , you must:

Making space travel more affordable can be done by reducing the cost of launch vehicles and other equipment. This can be done through innovation and competition.

Making space travel more accessible can be done by opening up space travel to a wider range of people, including scientists , engineers, entrepreneurs, and artists.

Making space travel more sustainable can be done by reducing the environmental impact of space travel. This can be done by using more efficient launch vehicles , by developing new technologies to reduce waste, and by recycling and reusing materials.

Rocket Lab can launch a rocket in New Zealand about once a week. This is because New Zealand has a relatively stable climate and a long coastline, which makes it ideal for launching rockets .

Rocket Lab manufactures its rocket engines using a process called additive manufacturing. This process allows them to create complex parts quickly and easily. They can make a rocket engine in about a week.

The most common cause of space junk is the collision of two objects in orbit. This can create a chain reaction, where each collision creates more debris. The Electron rocket helps to fix this problem by using a new type of rocket engine that is designed to reduce the amount of debris created during a launch.

Video: Sunita Williams of NASA Provides a Tour of the ISS Orbital Laboratory

Astronauts sleep in zero gravity by using sleeping bags that are attached to the walls of the space station. The sleep stations are located in the crew quarters.

Spacesuits have changed a lot since they were first designed in 1997. They are now made of lighter, stronger materials and they have more features, such as built-in computers and cameras.

The continent that the space station was viewing was Africa. Sunny knew this because she could see the Sahara Desert.

Spacecraft attach to the space station using a variety of methods. One common method is to use a grapple fixture, which is a device that is attached to the spacecraft and the space station. The grapple fixture is then used to pull the spacecraft into place.

Whether or not Sunny should have put her hair in a ponytail is a matter of personal opinion. Some people might think that it was necessary to keep her hair out of her face, while others might think that it was not necessary.

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Why there were no clear zones for using LB media with 1% olive oil for screening lipilytic bacteria

The reason for the clear zone may be that there is Insufficient lipolytic activity.

The absence of clear zones when using LB media with 1% olive oil for screening lipolytic bacteria could be due to Insufficient lipolytic activity.

Enzymes called lipases , which are produced by lipolytic bacteria, break down fats and oils. It's possible that the bacteria utilized in the experiment won't create lipases or have poor lipolytic activity, which would prevent them from efficiently hydrolyzing the olive oil in the media. There wouldn't be any observable clearing or lipid hydrolysis zones as a result.

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analyse how experiencing violence within a relationship could lead to psychological trauma​

Experiencing violence within a relationship could lead to psychological trauma ​ like anxiety disorders, depression , PTSD.

Psychological trauma can vary from person to person, but experiencing violence can affect a person's mental health which can lead to conditions like anxiety disorders , depression, PTSD.

Psychological trauma can interfere with a person's ability to function as they would under normal circumstances. Most of the people experience an increased level of emotional distress .

Thus, psychological trauma​ can lead to mental illness which can be associated with violence.

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help me please starting from 12 the labeling part till. 15 please

In the labeling process, starting from step 12, you need to carefully examine the data and assign appropriate labels to each instance. This involves categorizing or tagging the data based on predefined criteria or classes. The labeling process is crucial for supervised machine learning tasks as it provides the labeled examples necessary for the model to learn and make accurate predictions.

To begin, review the data and understand the labeling guidelines or instructions provided. This helps ensure consistency and accuracy throughout the process. Next, analyze each instance and assign the corresponding label based on the specific task at hand, such as sentiment analysis, object recognition, or text classification.

It is essential to be diligent and pay attention to detail while labeling to minimize errors and maintain the quality of the labeled dataset. Document any uncertainties or ambiguous instances for later review or clarification.

Finally, once all instances have been labeled, it is advisable to conduct a quality check or review to identify and correct any labeling mistakes or inconsistencies. This step helps guarantee the reliability and usability of the labeled dataset for training machine learning models.

In summary, the labeling process from step 12 to 15 involves carefully examining the data, assigning labels based on predefined criteria, ensuring consistency, and conducting a quality check to maintain the accuracy and reliability of the labeled dataset.

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I NEED HELP!! ASAP. PLEASE ANSWER QUICKLY 1. How might a field like philosophy or history help scientists? 2. Give an example of something that science cannot predict and explain why not. 3. How does the quote from Socrates, “True knowledge exists in knowing that you know nothing” relate to engineering? 4. Today, it is almost impossible to imagine a world without video games, yet not long ago they did not exist. What positive and negative social changes did they bring? 5. 3-D printers are one of the cutting edges developments in the technology of our time. How could they make their way into everyday life?

Check the attached document. The answer is more than 5,000 characters

Explanation:

Which revision changes the sentence to draw attention to the object of the action rather than the people or things performing the action? O The "space race" by competing scientists occurred during the Cold War. During the Cold War, scientists led the "space race" competition. ODuring the Cold War "space race," scientists competed. O The "space race" was a Cold War competition by scientists.

Answer & Explanation:

The revision that changes the sentence to draw attention to the object of the action rather than the people or things performing the action is:

"The 'space race' was a Cold War competition by scientists."

This sentence emphasizes the object of the action ("space race") by placing it at the beginning of the sentence and using the passive voice to downplay the importance of the subject ("scientists"). The focus is on the competition itself and the historical context in which it occurred, rather than the individuals who were involved in it.

Explain what traits you would give a pathogen if you wanted to make it hard for a vaccine to be used. List at least 4 things help please

Here are four traits that would make it hard for a vaccine to be used:

1. Rapid mutation rate . If a pathogen mutates rapidly, it will be able to evade the immune system's defenses, including the antibodies produced by a vaccine. This is a particular problem with viruses, which can mutate very quickly.

2. Ability to evade the immune system . Some pathogens are able to evade the immune system by hiding inside cells or by changing their surface proteins so that they are no longer recognized by the immune system. This makes it difficult for the immune system to mount an effective response to the infection.

3. Ability to spread easily . If a pathogen is easily spread from person to person, it will be more difficult to prevent infection through vaccination. This is a particular problem with respiratory viruses, which can be spread through coughing and sneezing.

4. Lack of animal reservoirs . If a pathogen does not have animal reservoirs, it will be more difficult to develop a vaccine against it. This is because vaccines are typically developed using weakened or killed versions of the pathogen. If there are no animal reservoirs, there will be no source of the pathogen to use for vaccine development.

It is important to note that these are just a few of the traits that can make it difficult to develop a vaccine against a pathogen. There are many other factors that can contribute to the difficulty of vaccine development, such as the cost of vaccine development, the availability of funding, and the political will to support vaccine development.

Describe what the immune system does, an example of how it can fail, and the name of a disease or disorder where it has failed. asappp

A failure of the immune system may lead to acquired immunodeficiency syndrome.

The body's defense against harmful pathogens like bacteria, viruses, and parasites as well as aberrant cells like cancer cells is provided by the immune system, a complex network of cells, tissues, and organs.

Its main job is to separate these foreign chemicals from the body's healthy cells and tissues in order to recognize and get rid of them.

HIV attacks and destroys specific immune cells , impairing the body's ability to fight off infections and diseases.

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Times New Romar 14 Á A Aa BIU al X, x² Funt = 1E EN T Paragraph AaBbCcDd AsBbCcDd AaBbG AaBbcct AaB AaBbCcD AaBbCcDd AaBbCcDd AaBbCcDd 1 Normal 1 No Spac... Heading 1 Heading 2 Title Subtitle Subtle Em... Emphasis Intense E.... Styles 06. An office worker is describing her journey from home every morning. Complete her description by putting in the correct prepositions. Don't repeat propositions twice. (over, up, across, to, through, a t, round, down, out of, by, on) I walk eg :-to work every day. Most of the people I work with go (1), I prefer to go (2) foot, because it's healthier. I come (3) _______ my apartment and then I take the lift (4) to the ground floor. I walk a block (5) the corner and then I go along the road to the corner. I walk and then I go (6) a shopping centre until I reach the other side of it. Then the road to the other side. Then I I come to some traffic lights. I go (7) reach a bridge that goes (8) to the bridgo My office building is (10) the main road. I go (9) the other gi car but the steps and the bridg C ab W

I walk eg: to work every day. Most of the people I work with go (1) by car, I prefer to go (2) on foot because it's healthier. I come (3) out of my apartment and then I take the lift (4) down to the ground floor. I walk a block (5) to the corner and then I go (6) through a shopping centre until I reach the other side of it. Then I go (7) across the road to the other side. Then I reach a bridge that goes (8) over to the bridge. My office building is (10) on the main road. I go (9) down the steps and across the bridge to the other side.

Adaptive features of cat fish eggs

Cat fish eggs has adaptive features such as adhesive eggs and protective coverings.

Catfish eggs have sticky exterior surfaces that allow them to stick to a variety of substrates, including rocks, vegetation, or the nest's walls. The eggs are kept anchored in place by their sticky quality, preventing water currents from carrying them away.

Catfish eggs are encased in a hard , gelatinous membrane or capsule as protection. The developing embryos are protected from potential physical harm, predators, and environmental challenges by this coating.

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A male who is homozygous for widow's peak and heterozygous for tongue rolling is crossed with a female without widow's peak and heterozygous for tongue rolling. Use the letter P for window's peak and R for tongue rolling. How many characteristics are being studied in this cross?​

The two traits segregate independently during the formation of gametes and will result in a combination of genotypes and phenotypes in the offspring.

In this cross, two characteristics are being studied: widow's peak and tongue rolling. Widow's peak is a genetic trait determined by a single gene, where the presence of the peak is denoted by the letter P, while the absence is denoted by the letter p. Tongue rolling is another genetic trait determined by a separate gene, where the ability to roll the tongue is denoted by the letter R, while the inability is denoted by the letter r.

The male in this cross is homozygous for widow's peak (PP) and heterozygous for tongue rolling (Rr). The female, on the other hand, lacks a widow's peak (pp) and is also heterozygous for tongue rolling (Rr).

Therefore, the cross involves the inheritance of two traits: widow's peak (P/p) and tongue rolling (R/r).

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The change to the investigation that would remove the source of the error noticed by the scientist is to test a fourth group , in which the participants do not receive the mineral supplement (option A).

A controlled experiment is an experiment designed to replicate the main experiment in every way, except in respect of the one variable that is being tested, so as isolate the effect of that variable.

In other words, a control is introduced in the experimentation process. A control is group of test subjects left untreated or unexposed to the independent variable.

According to this question, a company is testing the effect of a mineral supplement on common cold. The experimental procedure is okay except that they didn't include a fourth group, which will serve as the control .

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what is osmosis? what is plant osmosis?​

Osmosis is the movement of solvent molecules, usually water, across a selectively permeable membrane from an area of low solute concentration to an area of high solute concentration. The process continues until the solute concentrations are equal on both sides of the membrane. Osmosis is a critical process for the survival of cells, as it allows for the movement of water and nutrients into and out of cells.

Plant osmosis refers to the process of water movement in plants, which is critical for their growth and survival. Plant cells have a selectively permeable membrane called the cell membrane and a rigid cell wall that surrounds it. When a plant cell is placed in a solution with a lower solute concentration than the cell's cytoplasm, water will move into the cell through osmosis, causing the cell to swell. This swelling is called turgor pressure and is essential for maintaining the shape and structure of the plant. Conversely, when a plant cell is placed in a solution with a higher solute concentration than the cell's cytoplasm, water will move out of the cell, causing the cell to shrink. This loss of turgor pressure is known as plasmolysis and can lead to wilting and death of the plant.

3 kinds of water resources

Water Sources

What is the answer I don’t understand the question what is the answer

The conclusion that hand sanitizer is most effective in killing bacteria is not valid based on the data provided so C, The conclusion is not valid; there were fewer bacteria cultures on the soap plates .

The data shows that hand sanitizer had a higher number of bacteria cultures than warm water, but it also had a higher number of bacteria cultures than the soaps. This suggests that hand sanitizer is not necessarily more effective at killing bacteria than soap.

There are a few possible explanations for the higher number of bacteria cultures on the hand sanitizer plates. One possibility is that the hand sanitizer did not kill all of the bacteria on the hands. Another possibility is that the hand sanitizer killed some of the bacteria, but it also created a more favorable environment for the growth of other bacteria.

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Video: Washington State is Thinning Out Forests to Reduce Wildfire Risk. PLEASE ANSWER QUESTIONS ASAP!! 1. What is Russ Vaagen producing in his new sawmill? 2. How do his methods differ from logging that was done in the past? How does this help prevent fires? 3. How has Mike Peterson’s opinion on logging evolved? Why did he change his mind? 4. Do you think that this type of logging solution will work in the long run? Why or why not.

1. Russ Vaagen is producing cross-laminated timber (CLT) in his new sawmill. 2. Russ Vaagen's methods differ by selective thinning, reducing fuel load and creating healthier forests. 3. Mike Peterson changed his opinion after witnessing the positive results of selective thinning. 4. The long-term effectiveness depends on complementary fire prevention measures, scientific monitoring , and adaptive forest management practices.

1. Russ Vaagen is producing cross-laminated timber (CLT) in his new sawmill. CLT is a type of engineered wood product that is made by stacking layers of wood panels in alternating directions and bonding them together. It is known for its strength, durability, and sustainability, and it can be used in various construction applications.

2. Russ Vaagen's methods differ from logging in the past by focusing on selective thinning. Instead of clear-cutting large areas, Vaagen's approach involves removing smaller trees, primarily those that are less fire-resistant or overcrowded.

By doing so, he creates spacing between the remaining trees, which reduces competition for resources and increases the overall health of the forest.

This selective thinning helps prevent fires by reducing the fuel load, as smaller trees are often the ones that contribute the most to the intensity and spread of wildfires.

3. Mike Peterson's opinion on logging has evolved over time. Initially, he was skeptical about the effectiveness of logging as a wildfire prevention strategy. However, his perspective changed when he witnessed the positive results of Russ Vaagen's selective thinning methods.

Seeing the reduction in fuel load and the healthier forest ecosystem convinced Peterson of the benefits of this approach. The tangible outcomes and the scientific evidence supporting the effectiveness of selective thinning prompted him to change his mind.

4. The long-term effectiveness of this type of logging solution depends on various factors. Selective thinning and creating spacing between trees can be an effective strategy in reducing wildfire risk, as it reduces fuel load and promotes healthier forests.

However, it is crucial to implement such practices in conjunction with other fire prevention measures, such as controlled burns, fire-resistant landscaping , and community preparedness.

Additionally, monitoring and adapting the logging practices based on scientific research and environmental considerations are essential to ensure sustainable forest management.

While this approach shows promise, continuous evaluation, adaptation, and holistic fire management strategies are necessary for long-term success in mitigating wildfire risk.

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Explain the differences and similarities between the Digestive system and the Excretory system. Be sure to specify what they are individually responsible for. asapp

The digestive system and the excretory system are two separate systems in the human body with distinct functions   although they do share some connections .

Responsibility  The digestive system is responsible for the breakdown, absorption  and processing of food to extract nutrients and energy that the body needs for various functions.

Excretory system

Responsibility: the excretory system is responsible for eliminating waste products and toxins from the body.

The digestive system is primarily involved in the breakdown and absorption of nutrients from food  while the excretory system focuses on removing waste products from the body and regulating fluid and electrolyte balance.

Similarities

Both systems are involved in the elimination of waste products from the body. the digestive system eliminates undigested food  while the excretory system removes metabolic waste products

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scientist call this phospholipid bilayer a _____ _______ _______ because the cell membrane is flexible and is made up of many parts.

Answer:The lipid bilayer

Explanation: it is a type of membrane that separates the cell from the environment and is made of two layers of phospholipids. Also known as the phospholipid bilayer, 

Sonam is a hiring manager at a software company. Which of the applicants below would Sonam likely shortlist due to potential as a technology expert? 23-year-old Kai 37-year-old Greer 70-year-old Reese 65-year-old Perri

Sonam is a hiring manager at a software company. Sonam would likely consider Kai, the 23-year-old applicant, as a potential candidate for shortlisting.

The correct answer would be 23-year-old Kai.

As a hiring manager at a software company, Sonam would likely shortlist the applicant who demonstrates the most potential as a technology expert. While age alone should not be the sole criterion for shortlisting candidates, it is important to consider an individual's relevant skills, experience, and qualifications.

In this scenario, Sonam would likely consider Kai, the 23-year-old applicant, as a potential candidate for shortlisting. Being young, Kai may have recently acquired relevant education and training in the field of technology, making them well-versed in the latest advancements and trends. They may also have a fresh perspective and enthusiasm towards technology , which can be valuable in a dynamic industry like software development.

While age does not dictate someone's ability to excel in technology, older candidates like Greer (37), Reese (70), or Perri (65) might have more experience and expertise in the field. However, without additional information about their qualifications and technology-related skills, it is difficult to determine their potential as technology experts.

Ultimately, Sonam's decision should be based on a holistic evaluation of each candidate's qualifications, experience, and aptitude for the specific technology requirements of the position.

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Hearing is MOST acute at age: 30. 40. 10. 20.

You must have seen stored rice is attacked by Weevil. How can you get rid of this pest? List down 3 such pests that attack stored grains and the methods to remove them.

Yes, Weevils are a common pest that can infest stored rice and other grains. To get rid of Weevils, you can follow these methods:

Freezing: Freezing can kill any eggs, larvae, or adult weevils present in the rice. Place the rice in an airtight container and keep it in the freezer for at least four days.

Heat treatment: You can also heat the rice to kill any pests. Spread the rice in a thin layer on a baking sheet and bake it at 150°F for 30 minutes. This will kill any eggs or larvae present in the rice.

Bay leaves: Place a few bay leaves in the container with the rice. The strong smell of bay leaves is believed to repel weevils and prevent infestation.

Apart from Weevils, other common pests that can infest stored grains include:

Indian Meal Moth: They can be eliminated by discarding infested grains, cleaning the storage area, and using pheromone traps to capture the adult moths.

Grain Mites: Grain mites can be eliminated by reducing humidity levels in the storage area, discarding infested grains, and using diatomaceous earth or boric acid to kill the mites.

Rice and Maize Weevils: They can be controlled by cleaning the storage area, using pheromone traps to capture the adult weevils, and fumigating the storage area with insecticides.

1. Read: Discussion Background: Cold winter weather may cause additional breathing issues for those with respiratory problems if they catch a cold or the flu. Your family member, who has asthma, contacts you and tells you they have a cold and could hardly breathe but that she visited her doctor and were prescribed an inhaler and she feels much better now. When she picked up the prescription from the pharmacy there were two inhalers in the bag. Your family member thought the pharmacy made a mistake. 2. Initial Post: Create a new thread and answer all three parts of the initial prompt below A. Would or would you not provide any medical advice to your family member? B. Pharmacy technicians working in a retail pharmacy need to be able to help patients locate over-the-counter (OTC) nonprescription treatments for the common cold . How will you assists a patient that asks for an OTC medication for their common cold? C. Pharmacy technicians fill prescriptions for inhalers and must be able to calculate how many to dispense to the patient based on the prescriber’s dosing instructions. Explain why it is important to accurately calculate and dispense the correct amount to a sick patient.

A. It depends on the individual's knowledge and expertise in the medical field. If the individual is not a medical professional, it would be best to refrain from providing medical advice and instead encourage the family member to consult with their doctor for any medical concerns.

B. As a pharmacy technician working in a retail pharmacy, I would assist a patient asking for an OTC medication for their common cold by first asking about their symptoms and any pre-existing medical conditions or allergies. Based on this information, I would then recommend appropriate OTC medications that could help alleviate their symptoms.

C. It is important for pharmacy technicians to accurately calculate and dispense the correct amount of medication to a sick patient because incorrect dosages can result in ineffective treatment or even harm the patient. Ensuring that the patient receives the correct amount of medication as prescribed by their doctor is crucial for their recovery and well-being.

Identify one misconception about the theory of evolution.

Using natural selection, evolution is the process by which a species' characteristics change over several generations. The three constraints of Darwin's hypothesis concern the beginning of DNA, the unchangeable intricacy of the cell, and the lack of momentary species.

When a species splits into multiple new forms as a result of a shift in the environment that opens up new resources or introduces new environmental obstacles. Darwin's finches on the Galapagos Islands have created different molded snouts to exploit the various types of food accessible on various islands.

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2. The number of grizzly bear deaths in Alberta from 1976 to 1988 was estimated to be 581. Only 281 deaths were recorded from 1988 to 2000. How does this information affect the prediction you made in question 1? Explain your answer.

The data shows a significant decrease in grizzly bear deaths from 581 in the earlier period to 281 in the later period.

The information about grizzly bear deaths in Alberta from 1976 to 1988 and from 1988 to 2000 affects the prediction made in question 1 by providing additional data points and context.This decrease in grizzly bear deaths suggests a possible declining trend in the population . If the overall trend continues, it is reasonable to assume that the population has decreased further since 2000.

Taking this new information into account, the prediction made in question 1 should be revised. Instead of assuming a stable population or a slight increase, it would be more accurate to predict a decline in the grizzly bear population in Alberta. However, it is important to note that additional factors, such as conservation efforts or changes in habitat, should also be considered to gain a comprehensive understanding of the population dynamics .

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what causes rain during summer ​

Answer: Warmer air

Explanation: Air that is warmer is able to evaporate more water into the atmosphere.

Warmer heat

This is quite surprising, but air that is warmer can evaporate more water into the atmosphere. An air mass with more water vapor available to precipitate will naturally create more precipitation. Also, this causes air to rise, creating an intense  low-pressure condition  on the surface.

what kind of molecules are similar to the structure of zidovudine

Zidovudine|C10H13N5O4-PubChem

why does people afraid fat constitute foods?​

Cholesterol and saturated fats

Cholesterol is a fatty substance that's mostly made by the body in the liver.

It's carried in the blood as:

low-density lipoprotein (LDL)

high-density lipoprotein (HDL)

Eating too much saturated fats in your diet can raise "bad" LDL cholesterol in your blood, which can increase the risk of heart disease and stroke.

"Good" HDL cholesterol has a positive effect by taking cholesterol from parts of the body where there's too much of it to the liver, where it's disposed of.

A tectonic plate is part of the Earth’s lithosphere and can be best described as a large, easily broken block. a large, fairly rigid block. a small, easily eroded block. a small, fairly rigid block.

Answer: a. large, fairly rigid block.

A tectonic plate is part of the Earth’s lithosphere and can be best described as:

a large, fairly rigid block.

Tectonic plates are the large, rigid pieces of the Earth's lithosphere that fit together in a way similar to a jigsaw puzzle to form the Earth's crust. These plates move and interact at their boundaries, leading to various geological phenomena such as earthquakes, volcanic activity, and the creation of mountain ranges.

INCREASE THE LIGHT INTENSITY

•Slope ceiling used to direct more light into space.

•Avoid direct beam daylight on the critical visual task.

•Use of high-performance glazing.

•Design of daylight optimised fenestration.

How does the neuroendocrine system maintain homeostasis in the body? A. Neurons affect the endocrine system, while hormones affect the exocrine organs. B. Nerves and hormones both use steroids to affect the nervous system. C. Nerves monitor the body, and hormones make adjustments. D. It activates the somatic response and the sympathetic response.​

The correct answer is C. This coordinated system enables the body to respond to changing conditions and maintain a stable internal balance.

Nerves monitor the body, and hormones make adjustments. The neuroendocrine system, which consists of the nervous and endocrine systems, plays a crucial role in maintaining homeostasis in the body. Nerves, specifically sensory neurons, continuously monitor various physiological parameters such as temperature , blood pressure, pH levels, and hormone concentrations. These sensory signals are transmitted to the central nervous system (CNS), which interprets the information.

In response to the sensory input, the CNS initiates appropriate responses by releasing hormones from endocrine glands. These hormones are chemical messengers that travel through the bloodstream to target organs or tissues, where they exert their effects. By binding to specific receptors on target cells, hormones regulate various physiological processes, such as metabolism , growth, reproduction, and electrolyte balance. This allows the body to make necessary adjustments to maintain stability and ensure optimal functioning.

In summary, the neuroendocrine system maintains homeostasis by utilizing nerves to monitor the body's internal environment and hormones to make adjustments based on the information received.

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