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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

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The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

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

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

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.

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).

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.

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• Hypothesis Testing: Definition, Uses, Limitations + Examples

Hypothesis testing is as old as the scientific method and is at the heart of the research process. 

Research exists to validate or disprove assumptions about various phenomena. The process of validation involves testing and it is in this context that we will explore hypothesis testing. 

What is a Hypothesis? 

A hypothesis is a calculated prediction or assumption about a population parameter based on limited evidence. The whole idea behind hypothesis formulation is testing—this means the researcher subjects his or her calculated assumption to a series of evaluations to know whether they are true or false. 

Typically, every research starts with a hypothesis—the investigator makes a claim and experiments to prove that this claim is true or false . For instance, if you predict that students who drink milk before class perform better than those who don’t, then this becomes a hypothesis that can be confirmed or refuted using an experiment.  

Read: What is Empirical Research Study? [Examples & Method]

What are the Types of Hypotheses? 

1. simple hypothesis.

Also known as a basic hypothesis, a simple hypothesis suggests that an independent variable is responsible for a corresponding dependent variable. In other words, an occurrence of the independent variable inevitably leads to an occurrence of the dependent variable. 

Typically, simple hypotheses are considered as generally true, and they establish a causal relationship between two variables. 

Examples of Simple Hypothesis  

• Drinking soda and other sugary drinks can cause obesity. 
• Smoking cigarettes daily leads to lung cancer.

2. Complex Hypothesis

A complex hypothesis is also known as a modal. It accounts for the causal relationship between two independent variables and the resulting dependent variables. This means that the combination of the independent variables leads to the occurrence of the dependent variables . 

Examples of Complex Hypotheses  

• Adults who do not smoke and drink are less likely to develop liver-related conditions.
• Global warming causes icebergs to melt which in turn causes major changes in weather patterns.

3. Null Hypothesis

As the name suggests, a null hypothesis is formed when a researcher suspects that there’s no relationship between the variables in an observation. In this case, the purpose of the research is to approve or disapprove this assumption. 

Examples of Null Hypothesis

• This is no significant change in a student’s performance if they drink coffee or tea before classes. 
• There’s no significant change in the growth of a plant if one uses distilled water only or vitamin-rich water. 

Read: Research Report: Definition, Types + [Writing Guide]

4. Alternative Hypothesis 

To disapprove a null hypothesis, the researcher has to come up with an opposite assumption—this assumption is known as the alternative hypothesis. This means if the null hypothesis says that A is false, the alternative hypothesis assumes that A is true. 

An alternative hypothesis can be directional or non-directional depending on the direction of the difference. A directional alternative hypothesis specifies the direction of the tested relationship, stating that one variable is predicted to be larger or smaller than the null value while a non-directional hypothesis only validates the existence of a difference without stating its direction. 

Examples of Alternative Hypotheses  

• Starting your day with a cup of tea instead of a cup of coffee can make you more alert in the morning. 
• The growth of a plant improves significantly when it receives distilled water instead of vitamin-rich water. 

5. Logical Hypothesis

Logical hypotheses are some of the most common types of calculated assumptions in systematic investigations. It is an attempt to use your reasoning to connect different pieces in research and build a theory using little evidence. In this case, the researcher uses any data available to him, to form a plausible assumption that can be tested. 

Examples of Logical Hypothesis

• Waking up early helps you to have a more productive day. 
• Beings from Mars would not be able to breathe the air in the atmosphere of the Earth. 

6. Empirical Hypothesis  

After forming a logical hypothesis, the next step is to create an empirical or working hypothesis. At this stage, your logical hypothesis undergoes systematic testing to prove or disprove the assumption. An empirical hypothesis is subject to several variables that can trigger changes and lead to specific outcomes. 

Examples of Empirical Testing 

• People who eat more fish run faster than people who eat meat.
• Women taking vitamin E grow hair faster than those taking vitamin K.

7. Statistical Hypothesis

When forming a statistical hypothesis, the researcher examines the portion of a population of interest and makes a calculated assumption based on the data from this sample. A statistical hypothesis is most common with systematic investigations involving a large target audience. Here, it’s impossible to collect responses from every member of the population so you have to depend on data from your sample and extrapolate the results to the wider population. 

Examples of Statistical Hypothesis  

• 45% of students in Louisiana have middle-income parents. 
• 80% of the UK’s population gets a divorce because of irreconcilable differences.

What is Hypothesis Testing? 

Hypothesis testing is an assessment method that allows researchers to determine the plausibility of a hypothesis. It involves testing an assumption about a specific population parameter to know whether it’s true or false. These population parameters include variance, standard deviation, and median. 

Typically, hypothesis testing starts with developing a null hypothesis and then performing several tests that support or reject the null hypothesis. The researcher uses test statistics to compare the association or relationship between two or more variables. 

Explore: Research Bias: Definition, Types + Examples

Researchers also use hypothesis testing to calculate the coefficient of variation and determine if the regression relationship and the correlation coefficient are statistically significant.

How Hypothesis Testing Works

The basis of hypothesis testing is to examine and analyze the null hypothesis and alternative hypothesis to know which one is the most plausible assumption. Since both assumptions are mutually exclusive, only one can be true. In other words, the occurrence of a null hypothesis destroys the chances of the alternative coming to life, and vice-versa. 

Interesting: 21 Chrome Extensions for Academic Researchers in 2021

What Are The Stages of Hypothesis Testing?  

To successfully confirm or refute an assumption, the researcher goes through five (5) stages of hypothesis testing; 

• Determine the null hypothesis
• Specify the alternative hypothesis
• Set the significance level
• Calculate the test statistics and corresponding P-value
• Draw your conclusion
• Determine the Null Hypothesis

Like we mentioned earlier, hypothesis testing starts with creating a null hypothesis which stands as an assumption that a certain statement is false or implausible. For example, the null hypothesis (H0) could suggest that different subgroups in the research population react to a variable in the same way. 

• Specify the Alternative Hypothesis

Once you know the variables for the null hypothesis, the next step is to determine the alternative hypothesis. The alternative hypothesis counters the null assumption by suggesting the statement or assertion is true. Depending on the purpose of your research, the alternative hypothesis can be one-sided or two-sided. 

Using the example we established earlier, the alternative hypothesis may argue that the different sub-groups react differently to the same variable based on several internal and external factors. 

• Set the Significance Level

Many researchers create a 5% allowance for accepting the value of an alternative hypothesis, even if the value is untrue. This means that there is a 0.05 chance that one would go with the value of the alternative hypothesis, despite the truth of the null hypothesis. 

Something to note here is that the smaller the significance level, the greater the burden of proof needed to reject the null hypothesis and support the alternative hypothesis.

Explore: What is Data Interpretation? + [Types, Method & Tools]

• Calculate the Test Statistics and Corresponding P-Value 

Test statistics in hypothesis testing allow you to compare different groups between variables while the p-value accounts for the probability of obtaining sample statistics if your null hypothesis is true. In this case, your test statistics can be the mean, median and similar parameters. 

If your p-value is 0.65, for example, then it means that the variable in your hypothesis will happen 65 in100 times by pure chance. Use this formula to determine the p-value for your data: 

• Draw Your Conclusions

After conducting a series of tests, you should be able to agree or refute the hypothesis based on feedback and insights from your sample data.  

Applications of Hypothesis Testing in Research

Hypothesis testing isn’t only confined to numbers and calculations; it also has several real-life applications in business, manufacturing, advertising, and medicine. 

In a factory or other manufacturing plants, hypothesis testing is an important part of quality and production control before the final products are approved and sent out to the consumer. 

During ideation and strategy development, C-level executives use hypothesis testing to evaluate their theories and assumptions before any form of implementation. For example, they could leverage hypothesis testing to determine whether or not some new advertising campaign, marketing technique, etc. causes increased sales. 

In addition, hypothesis testing is used during clinical trials to prove the efficacy of a drug or new medical method before its approval for widespread human usage. 

What is an Example of Hypothesis Testing?

An employer claims that her workers are of above-average intelligence. She takes a random sample of 20 of them and gets the following results: 

Mean IQ Scores: 110

Standard Deviation: 15 

Mean Population IQ: 100

Step 1: Using the value of the mean population IQ, we establish the null hypothesis as 100.

Step 2: State that the alternative hypothesis is greater than 100.

Step 3: State the alpha level as 0.05 or 5% 

Step 4: Find the rejection region area (given by your alpha level above) from the z-table. An area of .05 is equal to a z-score of 1.645.

Step 5: Calculate the test statistics using this formula

Z = (110–100) ÷ (15÷√20) 

10 ÷ 3.35 = 2.99 

If the value of the test statistics is higher than the value of the rejection region, then you should reject the null hypothesis. If it is less, then you cannot reject the null. 

In this case, 2.99 > 1.645 so we reject the null. 

Importance/Benefits of Hypothesis Testing 

The most significant benefit of hypothesis testing is it allows you to evaluate the strength of your claim or assumption before implementing it in your data set. Also, hypothesis testing is the only valid method to prove that something “is or is not”. Other benefits include: 

• Hypothesis testing provides a reliable framework for making any data decisions for your population of interest. 
• It helps the researcher to successfully extrapolate data from the sample to the larger population. 
• Hypothesis testing allows the researcher to determine whether the data from the sample is statistically significant. 
• Hypothesis testing is one of the most important processes for measuring the validity and reliability of outcomes in any systematic investigation. 
• It helps to provide links to the underlying theory and specific research questions.

Criticism and Limitations of Hypothesis Testing

Several limitations of hypothesis testing can affect the quality of data you get from this process. Some of these limitations include: 

• The interpretation of a p-value for observation depends on the stopping rule and definition of multiple comparisons. This makes it difficult to calculate since the stopping rule is subject to numerous interpretations, plus “multiple comparisons” are unavoidably ambiguous. 
• Conceptual issues often arise in hypothesis testing, especially if the researcher merges Fisher and Neyman-Pearson’s methods which are conceptually distinct. 
• In an attempt to focus on the statistical significance of the data, the researcher might ignore the estimation and confirmation by repeated experiments.
• Hypothesis testing can trigger publication bias, especially when it requires statistical significance as a criterion for publication.
• When used to detect whether a difference exists between groups, hypothesis testing can trigger absurd assumptions that affect the reliability of your observation.

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A Beginner’s Guide to Hypothesis Testing in Business

• 30 Mar 2021

Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.

If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.

Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.

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What Is Hypothesis Testing?

To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.

A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”

Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.

Hypothesis Testing in Business

When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.

The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.

As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.

In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.

Related: 9 Fundamental Data Science Skills for Business Professionals

Key Considerations for Hypothesis Testing

1. alternative hypothesis and null hypothesis.

In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.

For example, consider a company’s leadership team that historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.

In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”

The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.

Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.

2. Significance Level and P-Value

Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.

With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.

In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.

3. One-Sided vs. Two-Sided Testing

When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.

Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.

4. Sampling

To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.

A survey involves asking a series of questions to a random population sample and recording self-reported responses.

Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.

Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.

Learn How to Perform Hypothesis Testing

Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.

If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.

Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .

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• Product Management

How to Generate and Validate Product Hypotheses

What is a product hypothesis.

A hypothesis is a testable statement that predicts the relationship between two or more variables. In product development, we generate hypotheses to validate assumptions about customer behavior, market needs, or the potential impact of product changes. These experimental efforts help us refine the user experience and get closer to finding a product-market fit.

Product hypotheses are a key element of data-driven product development and decision-making. Testing them enables us to solve problems more efficiently and remove our own biases from the solutions we put forward.

Here’s an example: ‘If we improve the page load speed on our website (variable 1), then we will increase the number of signups by 15% (variable 2).’ So if we improve the page load speed, and the number of signups increases, then our hypothesis has been proven. If the number did not increase significantly (or not at all), then our hypothesis has been disproven.

In general, product managers are constantly creating and testing hypotheses. But in the context of new product development , hypothesis generation/testing occurs during the validation stage, right after idea screening .

Now before we go any further, let’s get one thing straight: What’s the difference between an idea and a hypothesis?

Idea vs hypothesis

Innovation expert Michael Schrage makes this distinction between hypotheses and ideas – unlike an idea, a hypothesis comes with built-in accountability. “But what’s the accountability for a good idea?” Schrage asks. “The fact that a lot of people think it’s a good idea? That’s a popularity contest.” So, not only should a hypothesis be tested, but by its very nature, it can be tested.

At Railsware, we’ve built our product development services on the careful selection, prioritization, and validation of ideas. Here’s how we distinguish between ideas and hypotheses:

Idea: A creative suggestion about how we might exploit a gap in the market, add value to an existing product, or bring attention to our product. Crucially, an idea is just a thought. It can form the basis of a hypothesis but it is not necessarily expected to be proven or disproven.

• We should get an interview with the CEO of our company published on TechCrunch.
• Why don’t we redesign our website?
• The Coupler.io team should create video tutorials on how to export data from different apps, and publish them on YouTube.
• Why not add a new ‘email templates’ feature to our Mailtrap product?

Hypothesis: A way of framing an idea or assumption so that it is testable, specific, and aligns with our wider product/team/organizational goals.

Examples: 

• If we add a new ‘email templates’ feature to Mailtrap, we’ll see an increase in active usage of our email-sending API.
• Creating relevant video tutorials and uploading them to YouTube will lead to an increase in Coupler.io signups.
• If we publish an interview with our CEO on TechCrunch, 500 people will visit our website and 10 of them will install our product.

Now, it’s worth mentioning that not all hypotheses require testing . Sometimes, the process of creating hypotheses is just an exercise in critical thinking. And the simple act of analyzing your statement tells whether you should run an experiment or not. Remember: testing isn’t mandatory, but your hypotheses should always be inherently testable.

Let’s consider the TechCrunch article example again. In that hypothesis, we expect 500 readers to visit our product website, and a 2% conversion rate of those unique visitors to product users i.e. 10 people. But is that marginal increase worth all the effort? Conducting an interview with our CEO, creating the content, and collaborating with the TechCrunch content team – all of these tasks take time (and money) to execute. And by formulating that hypothesis, we can clearly see that in this case, the drawbacks (efforts) outweigh the benefits. So, no need to test it.

In a similar vein, a hypothesis statement can be a tool to prioritize your activities based on impact. We typically use the following criteria:

• The quality of impact
• The size of the impact
• The probability of impact

This lets us organize our efforts according to their potential outcomes – not the coolness of the idea, its popularity among the team, etc.

Now that we’ve established what a product hypothesis is, let’s discuss how to create one.

Start with a problem statement

Before you jump into product hypothesis generation, we highly recommend formulating a problem statement. This is a short, concise description of the issue you are trying to solve. It helps teams stay on track as they formalize the hypothesis and design the product experiments. It can also be shared with stakeholders to ensure that everyone is on the same page.

The statement can be worded however you like, as long as it’s actionable, specific, and based on data-driven insights or research. It should clearly outline the problem or opportunity you want to address.

Here’s an example: Our bounce rate is high (more than 90%) and we are struggling to convert website visitors into actual users. How might we improve site performance to boost our conversion rate?

How to generate product hypotheses

Now let’s explore some common, everyday scenarios that lead to product hypothesis generation. For our teams here at Railsware, it’s when:

• There’s a problem with an unclear root cause e.g. a sudden drop in one part of the onboarding funnel. We identify these issues by checking our product metrics or reviewing customer complaints.
• We are running ideation sessions on how to reach our goals (increase MRR, increase the number of users invited to an account, etc.)
• We are exploring growth opportunities e.g. changing a pricing plan, making product improvements , breaking into a new market.
• We receive customer feedback. For example, some users have complained about difficulties setting up a workspace within the product. So, we build a hypothesis on how to help them with the setup.

BRIDGES framework for ideation

When we are tackling a complex problem or looking for ways to grow the product, our teams use BRIDGeS – a robust decision-making and ideation framework. BRIDGeS makes our product discovery sessions more efficient. It lets us dive deep into the context of our problem so that we can develop targeted solutions worthy of testing.

Between 2-8 stakeholders take part in a BRIDGeS session. The ideation sessions are usually led by a product manager and can include other subject matter experts such as developers, designers, data analysts, or marketing specialists. You can use a virtual whiteboard such as Figjam or Miro (see our Figma template ) to record each colored note.

In the first half of a BRIDGeS session, participants examine the Benefits, Risks, Issues, and Goals of their subject in the ‘Problem Space.’ A subject is anything that is being described or dealt with; for instance, Coupler.io’s growth opportunities. Benefits are the value that a future solution can bring, Risks are potential issues they might face, Issues are their existing problems, and Goals are what the subject hopes to gain from the future solution. Each descriptor should have a designated color.

After we have broken down the problem using each of these descriptors, we move into the Solution Space. This is where we develop solution variations based on all of the benefits/risks/issues identified in the Problem Space (see the Uber case study for an in-depth example).

In the Solution Space, we start prioritizing those solutions and deciding which ones are worthy of further exploration outside of the framework – via product hypothesis formulation and testing, for example. At the very least, after the session, we will have a list of epics and nested tasks ready to add to our product roadmap.

How to write a product hypothesis statement

Across organizations, product hypothesis statements might vary in their subject, tone, and precise wording. But some elements never change. As we mentioned earlier, a hypothesis statement must always have two or more variables and a connecting factor.

1. Identify variables

Since these components form the bulk of a hypothesis statement, let’s start with a brief definition.

First of all, variables in a hypothesis statement can be split into two camps: dependent and independent. Without getting too theoretical, we can describe the independent variable as the cause, and the dependent variable as the effect . So in the Mailtrap example we mentioned earlier, the ‘add email templates feature’ is the cause i.e. the element we want to manipulate. Meanwhile, ‘increased usage of email sending API’ is the effect i.e the element we will observe.

Independent variables can be any change you plan to make to your product. For example, tweaking some landing page copy, adding a chatbot to the homepage, or enhancing the search bar filter functionality.

Dependent variables are usually metrics. Here are a few that we often test in product development:

• Number of sign-ups
• Number of purchases
• Activation rate (activation signals differ from product to product)
• Number of specific plans purchased
• Feature usage (API activation, for example)
• Number of active users

Bear in mind that your concept or desired change can be measured with different metrics. Make sure that your variables are well-defined, and be deliberate in how you measure your concepts so that there’s no room for misinterpretation or ambiguity.

For example, in the hypothesis ‘Users drop off because they find it hard to set up a project’ variables are poorly defined. Phrases like ‘drop off’ and ‘hard to set up’ are too vague. A much better way of saying it would be: If project automation rules are pre-defined (email sequence to responsible, scheduled tickets creation), we’ll see a decrease in churn. In this example, it’s clear which dependent variable has been chosen and why.

And remember, when product managers focus on delighting users and building something of value, it’s easier to market and monetize it. That’s why at Railsware, our product hypotheses often focus on how to increase the usage of a feature or product. If users love our product(s) and know how to leverage its benefits, we can spend less time worrying about how to improve conversion rates or actively grow our revenue, and more time enhancing the user experience and nurturing our audience.

2. Make the connection

The relationship between variables should be clear and logical. If it’s not, then it doesn’t matter how well-chosen your variables are – your test results won’t be reliable.

To demonstrate this point, let’s explore a previous example again: page load speed and signups.

Through prior research, you might already know that conversion rates are 3x higher for sites that load in 1 second compared to sites that take 5 seconds to load. Since there appears to be a strong connection between load speed and signups in general, you might want to see if this is also true for your product.

Here are some common pitfalls to avoid when defining the relationship between two or more variables:

Relationship is weak. Let’s say you hypothesize that an increase in website traffic will lead to an increase in sign-ups. This is a weak connection since website visitors aren’t necessarily motivated to use your product; there are more steps involved. A better example is ‘If we change the CTA on the pricing page, then the number of signups will increase.’ This connection is much stronger and more direct.

Relationship is far-fetched. This often happens when one of the variables is founded on a vanity metric. For example, increasing the number of social media subscribers will lead to an increase in sign-ups. However, there’s no particular reason why a social media follower would be interested in using your product. Oftentimes, it’s simply your social media content that appeals to them (and your audience isn’t interested in a product).

Variables are co-dependent. Variables should always be isolated from one another. Let’s say we removed the option “Register with Google” from our app. In this case, we can expect fewer users with Google workspace accounts to register. Obviously, it’s because there’s a direct dependency between variables (no registration with Google→no users with Google workspace accounts).

3. Set validation criteria

First, build some confirmation criteria into your statement . Think in terms of percentages (e.g. increase/decrease by 5%) and choose a relevant product metric to track e.g. activation rate if your hypothesis relates to onboarding. Consider that you don’t always have to hit the bullseye for your hypothesis to be considered valid. Perhaps a 3% increase is just as acceptable as a 5% one. And it still proves that a connection between your variables exists.

Secondly, you should also make sure that your hypothesis statement is realistic . Let’s say you have a hypothesis that ‘If we show users a banner with our new feature, then feature usage will increase by 10%.’ A few questions to ask yourself are: Is 10% a reasonable increase, based on your current feature usage data? Do you have the resources to create the tests (experimenting with multiple variations, distributing on different channels: in-app, emails, blog posts)?

Null hypothesis and alternative hypothesis

In statistical research, there are two ways of stating a hypothesis: null or alternative. But this scientific method has its place in hypothesis-driven development too…

Alternative hypothesis: A statement that you intend to prove as being true by running an experiment and analyzing the results. Hint: it’s the same as the other hypothesis examples we’ve described so far.

Example: If we change the landing page copy, then the number of signups will increase.

Null hypothesis: A statement you want to disprove by running an experiment and analyzing the results. It predicts that your new feature or change to the user experience will not have the desired effect.

Example: The number of signups will not increase if we make a change to the landing page copy.

What’s the point? Well, let’s consider the phrase ‘innocent until proven guilty’ as a version of a null hypothesis. We don’t assume that there is any relationship between the ‘defendant’ and the ‘crime’ until we have proof. So, we run a test, gather data, and analyze our findings — which gives us enough proof to reject the null hypothesis and validate the alternative. All of this helps us to have more confidence in our results.

Now that you have generated your hypotheses, and created statements, it’s time to prepare your list for testing.

Prioritizing hypotheses for testing

Not all hypotheses are created equal. Some will be essential to your immediate goal of growing the product e.g. adding a new data destination for Coupler.io. Others will be based on nice-to-haves or small fixes e.g. updating graphics on the website homepage.

Prioritization helps us focus on the most impactful solutions as we are building a product roadmap or narrowing down the backlog . To determine which hypotheses are the most critical, we use the MoSCoW framework. It allows us to assign a level of urgency and importance to each product hypothesis so we can filter the best 3-5 for testing.

MoSCoW is an acronym for Must-have, Should-have, Could-have, and Won’t-have. Here’s a breakdown:

• Must-have – hypotheses that must be tested, because they are strongly linked to our immediate project goals.
• Should-have – hypotheses that are closely related to our immediate project goals, but aren’t the top priority.
• Could-have – hypotheses of nice-to-haves that can wait until later for testing. 
• Won’t-have – low-priority hypotheses that we may or may not test later on when we have more time.

How to test product hypotheses

Once you have selected a hypothesis, it’s time to test it. This will involve running one or more product experiments in order to check the validity of your claim.

The tricky part is deciding what type of experiment to run, and how many. Ultimately, this all depends on the subject of your hypothesis – whether it’s a simple copy change or a whole new feature. For instance, it’s not necessary to create a clickable prototype for a landing page redesign. In that case, a user-wide update would do.

On that note, here are some of the approaches we take to hypothesis testing at Railsware:

A/B testing

A/B or split testing involves creating two or more different versions of a webpage/feature/functionality and collecting information about how users respond to them.

Let’s say you wanted to validate a hypothesis about the placement of a search bar on your application homepage. You could design an A/B test that shows two different versions of that search bar’s placement to your users (who have been split equally into two camps: a control group and a variant group). Then, you would choose the best option based on user data. A/B tests are suitable for testing responses to user experience changes, especially if you have more than one solution to test.

Prototyping

When it comes to testing a new product design, prototyping is the method of choice for many Lean startups and organizations. It’s a cost-effective way of collecting feedback from users, fast, and it’s possible to create prototypes of individual features too. You may take this approach to hypothesis testing if you are working on rolling out a significant new change e.g adding a brand-new feature, redesigning some aspect of the user flow, etc. To control costs at this point in the new product development process , choose the right tools — think Figma for clickable walkthroughs or no-code platforms like Bubble.

Deliveroo feature prototype example

Let’s look at how feature prototyping worked for the food delivery app, Deliveroo, when their product team wanted to ‘explore personalized recommendations, better filtering and improved search’ in 2018. To begin, they created a prototype of the customer discovery feature using web design application, Framer.

One of the most important aspects of this feature prototype was that it contained live data — real restaurants, real locations. For test users, this made the hypothetical feature feel more authentic. They were seeing listings and recommendations for real restaurants in their area, which helped immerse them in the user experience, and generate more honest and specific feedback. Deliveroo was then able to implement this feedback in subsequent iterations.

Asking your users

Interviewing customers is an excellent way to validate product hypotheses. It’s a form of qualitative testing that, in our experience, produces better insights than user surveys or general user research. Sessions are typically run by product managers and involve asking  in-depth interview questions  to one customer at a time. They can be conducted in person or online (through a virtual call center , for instance) and last anywhere between 30 minutes to 1 hour.

Although CustDev interviews may require more effort to execute than other tests (the process of finding participants, devising questions, organizing interviews, and honing interview skills can be time-consuming), it’s still a highly rewarding approach. You can quickly validate assumptions by asking customers about their pain points, concerns, habits, processes they follow, and analyzing how your solution fits into all of that.

Wizard of Oz

The Wizard of Oz approach is suitable for gauging user interest in new features or functionalities. It’s done by creating a prototype of a fake or future feature and monitoring how your customers or test users interact with it.

For example, you might have a hypothesis that your number of active users will increase by 15% if you introduce a new feature. So, you design a new bare-bones page or simple button that invites users to access it. But when they click on the button, a pop-up appears with a message such as ‘coming soon.’

By measuring the frequency of those clicks, you could learn a lot about the demand for this new feature/functionality. However, while these tests can deliver fast results, they carry the risk of backfiring. Some customers may find fake features misleading, making them less likely to engage with your product in the future.

User-wide updates

One of the speediest ways to test your hypothesis is by rolling out an update for all users. It can take less time and effort to set up than other tests (depending on how big of an update it is). But due to the risk involved, you should stick to only performing these kinds of tests on small-scale hypotheses. Our teams only take this approach when we are almost certain that our hypothesis is valid.

For example, we once had an assumption that the name of one of Mailtrap ’s entities was the root cause of a low activation rate. Being an active Mailtrap customer meant that you were regularly sending test emails to a place called ‘Demo Inbox.’ We hypothesized that the name was confusing (the word ‘demo’ implied it was not the main inbox) and this was preventing new users from engaging with their accounts. So, we updated the page, changed the name to ‘My Inbox’ and added some ‘to-do’ steps for new users. We saw an increase in our activation rate almost immediately, validating our hypothesis.

Feature flags

Creating feature flags involves only releasing a new feature to a particular subset or small percentage of users. These features come with a built-in kill switch; a piece of code that can be executed or skipped, depending on who’s interacting with your product.

Since you are only showing this new feature to a selected group, feature flags are an especially low-risk method of testing your product hypothesis (compared to Wizard of Oz, for example, where you have much less control). However, they are also a little bit more complex to execute than the others — you will need to have an actual coded product for starters, as well as some technical knowledge, in order to add the modifiers ( only when… ) to your new coded feature.

Let’s revisit the landing page copy example again, this time in the context of testing.

So, for the hypothesis ‘If we change the landing page copy, then the number of signups will increase,’ there are several options for experimentation. We could share the copy with a small sample of our users, or even release a user-wide update. But A/B testing is probably the best fit for this task. Depending on our budget and goal, we could test several different pieces of copy, such as:

• The current landing page copy
• Copy that we paid a marketing agency 10 grand for
• Generic copy we wrote ourselves, or removing most of the original copy – just to see how making even a small change might affect our numbers.

Remember, every hypothesis test must have a reasonable endpoint. The exact length of the test will depend on the type of feature/functionality you are testing, the size of your user base, and how much data you need to gather. Just make sure that the experiment running time matches the hypothesis scope. For instance, there is no need to spend 8 weeks experimenting with a piece of landing page copy. That timeline is more appropriate for say, a Wizard of Oz feature.

Recording hypotheses statements and test results

Finally, it’s time to talk about where you will write down and keep track of your hypotheses. Creating a single source of truth will enable you to track all aspects of hypothesis generation and testing with ease.

At Railsware, our product managers create a document for each individual hypothesis, using tools such as Coda or Google Sheets. In that document, we record the hypothesis statement, as well as our plans, process, results, screenshots, product metrics, and assumptions.

We share this document with our team and stakeholders, to ensure transparency and invite feedback. It’s also a resource we can refer back to when we are discussing a new hypothesis — a place where we can quickly access information relating to a previous test.

Understanding test results and taking action

The other half of validating product hypotheses involves evaluating data and drawing reasonable conclusions based on what you find. We do so by analyzing our chosen product metric(s) and deciding whether there is enough data available to make a solid decision. If not, we may extend the test’s duration or run another one. Otherwise, we move forward. An experimental feature becomes a real feature, a chatbot gets implemented on the customer support page, and so on.

Something to keep in mind: the integrity of your data is tied to how well the test was executed, so here are a few points to consider when you are testing and analyzing results:

Gather and analyze data carefully. Ensure that your data is clean and up-to-date when running quantitative tests and tracking responses via analytics dashboards. If you are doing customer interviews, make sure to record the meetings (with consent) so that your notes will be as accurate as possible.

Conduct the right amount of product experiments. It can take more than one test to determine whether your hypothesis is valid or invalid. However, don’t waste too much time experimenting in the hopes of getting the result you want. Know when to accept the evidence and move on.

Choose the right audience segment. Don’t cast your net too wide. Be specific about who you want to collect data from prior to running the test. Otherwise, your test results will be misleading and you won’t learn anything new.

Watch out for bias. Avoid confirmation bias at all costs. Don’t make the mistake of including irrelevant data just because it bolsters your results. For example, if you are gathering data about how users are interacting with your product Monday-Friday, don’t include weekend data just because doing so would alter the data and ‘validate’ your hypothesis.

• Not all failed hypotheses should be treated as losses. Even if you didn’t get the outcome you were hoping for, you may still have improved your product. Let’s say you implemented SSO authentication for premium users, but unfortunately, your free users didn’t end up switching to premium plans. In this case, you still added value to the product by streamlining the login process for paying users.
• Yes, taking a hypothesis-driven approach to product development is important. But remember, you don’t have to test everything . Use common sense first. For example, if your website copy is confusing and doesn’t portray the value of the product, then you should still strive to replace it with better copy – regardless of how this affects your numbers in the short term.

Wrapping Up

The process of generating and validating product hypotheses is actually pretty straightforward once you’ve got the hang of it. All you need is a valid question or problem, a testable statement, and a method of validation. Sure, hypothesis-driven development requires more of a time commitment than just ‘giving it a go.’ But ultimately, it will help you tune the product to the wants and needs of your customers.

If you share our data-driven approach to product development and engineering, check out our services page to learn more about how we work with our clients!

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Hypothesis Testing Steps & Examples

Table of Contents

What is a Hypothesis testing?

As per the definition from Oxford languages, a hypothesis is a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation. As per the Dictionary page on Hypothesis , Hypothesis means a proposition or set of propositions, set forth as an explanation for the occurrence of some specified group of phenomena, either asserted merely as a provisional conjecture to guide investigation (working hypothesis) or accepted as highly probable in the light of established facts.

The hypothesis can be defined as the claim that can either be related to the truth about something that exists in the world, or, truth about something that’s needs to be established a fresh . In simple words, another word for the hypothesis is the “claim” . Until the claim is proven to be true, it is called the hypothesis. Once the claim is proved, it becomes the new truth or new knowledge about the thing. For example , let’s say that a claim is made that students studying for more than 6 hours a day gets more than 90% of marks in their examination. Now, this is just a claim or a hypothesis and not the truth in the real world. However, in order for the claim to become the truth for widespread adoption, it needs to be proved using pieces of evidence, e.g., data.  In order to reject this claim or otherwise, one needs to do some empirical analysis by gathering data samples and evaluating the claim. The process of gathering data and evaluating the claims or hypotheses with the goal to reject or otherwise (failing to reject) can be called as hypothesis testing . Note the wordings – “failing to reject”. It means that we don’t have enough evidence to reject the claim. Thus, until the time that new evidence comes up, the claim can be considered the truth. There are different techniques to test the hypothesis in order to reach the conclusion of whether the hypothesis can be used to represent the truth of the world.

One must note that the hypothesis testing never constitutes a proof that the hypothesis is absolute truth based on the observations. It only provides added support to consider the hypothesis as truth until the time that new evidences can against the hypotheses can be gathered. We can never be 100% sure about truth related to those hypotheses based on the hypothesis testing.

Simply speaking, hypothesis testing is a framework that can be used to assert whether the claim or the hypothesis made about a real-world/real-life event can be seen as the truth or otherwise based on the given data (evidences).

Hypothesis Testing Examples

Before we get ahead and start understanding more details about hypothesis and hypothesis testing steps, lets take a look at some  real-world examples  of how to think about hypothesis and hypothesis testing when dealing with real-world problems :

• Customers are churning because they ain’t getting response to their complaints or issues
• Customers are churning because there are other competitive services in the market which are providing these services at lower cost.
• Customers are churning because there are other competitive services which are providing more services at the same cost.
• It is claimed that a 500 gm sugar packet for a particular brand, say XYZA, contains sugar of less than 500 gm, say around 480gm.  Can this claim be taken as truth? How do we know that this claim is true? This is a hypothesis until proved.
• A group of doctors claims that quitting smoking increases lifespan. Can this claim be taken as new truth? The hypothesis is that quitting smoking results in an increase in lifespan.
• It is claimed that brisk walking for half an hour every day reverses diabetes. In order to accept this in your lifestyle, you may need evidence that supports this claim or hypothesis.
• It is claimed that doing Pranayama yoga for 30 minutes a day can help in easing stress by 50%. This can be termed as hypothesis and would require testing / validation for it to be established as a truth and recommended for widespread adoption.
• One common real-life example of hypothesis testing is election polling. In order to predict the outcome of an election, pollsters take a sample of the population and ask them who they plan to vote for. They then use hypothesis testing to assess whether their sample is representative of the population as a whole. If the results of the hypothesis test are significant, it means that the sample is representative and that the poll can be used to predict the outcome of the election. However, if the results are not significant, it means that the sample is not representative and that the poll should not be used to make predictions.
• Machine learning models make predictions based on the input data. Each of the machine learning model representing a function approximation can be taken as a hypothesis. All different models constitute what is called as hypothesis space .
• As part of a linear regression machine learning model , it is claimed that there is a relationship between the response variables and predictor variables? Can this hypothesis or claim be taken as truth? Let’s say, the hypothesis is that the housing price depends upon the average income of people already staying in the locality. How true is this hypothesis or claim? The relationship between response variable and each of the predictor variables can be evaluated using T-test and T-statistics .
• For linear regression model , one of the hypothesis is that there is no relationship between the response variable and any of the predictor variables. Thus, if b1, b2, b3 are three parameters, all of them is equal to 0. b1 = b2 = b3 = 0. This is where one performs F-test and use F-statistics to test this hypothesis.

You may note different hypotheses which are listed above. The next step would be validate some of these hypotheses. This is where data scientists will come into picture. One or more data scientists may be asked to work on different hypotheses. This would result in these data scientists looking for appropriate data related to the hypothesis they are working. This section will be detailed out in near future.

State the Hypothesis to begin Hypothesis Testing

The first step to hypothesis testing is defining or stating a hypothesis. Before the hypothesis can be tested, we need to formulate the hypothesis in terms of mathematical expressions. There are two important aspects to pay attention to, prior to the formulation of the hypothesis. The following represents different types of hypothesis that could be put to hypothesis testing:

• Claim made against the well-established fact : The case in which a fact is well-established, or accepted as truth or “knowledge” and a new claim is made about this well-established fact. For example , when you buy a packet of 500 gm of sugar, you assume that the packet does contain at the minimum 500 gm of sugar and not any less, based on the label of 500 gm on the packet. In this case, the fact is given or assumed to be the truth. A new claim can be made that the 500 gm sugar contains sugar weighing less than 500 gm. This claim needs to be tested before it is accepted as truth. Such cases could be considered for hypothesis testing if this is claimed that the assumption or the default state of being is not true. The claim to be established as new truth can be stated as “alternate hypothesis”. The opposite state can be stated as “null hypothesis”. Here the claim that the 500 gm packet consists of sugar less than 500 grams would be stated as alternate hypothesis. The opposite state which is the sugar packet consists 500 gm is null hypothesis.
• Claim to establish the new truth : The case in which there is some claim made about the reality that exists in the world (fact). For example , the fact that the housing price depends upon the average income of people already staying in the locality can be considered as a claim and not assumed to be true. Another example could be the claim that running 5 miles a day would result in a reduction of 10 kg of weight within a month. There could be varied such claims which when required to be proved as true have to go through hypothesis testing. The claim to be established as new truth can be stated as “alternate hypothesis”. The opposite state can be stated as “null hypothesis”. Running 5 miles a day would result in reduction of 10 kg within a month would be stated as alternate hypothesis.

Based on the above considerations, the following hypothesis can be stated for doing hypothesis testing.

• The packet of 500 gm of sugar contains sugar of weight less than 500 gm. (Claim made against the established fact). This is a new knowledge which requires hypothesis testing to get established and acted upon.
• The housing price depends upon the average income of the people staying in the locality. This is a new knowledge which requires hypothesis testing to get established and acted upon.
• Running 5 miles a day results in a reduction of 10 kg of weight within a month. This is a new knowledge which requires hypothesis testing to get established for widespread adoption.

Formulate Null & Alternate Hypothesis as Next Step

Once the hypothesis is defined or stated, the next step is to formulate the null and alternate hypothesis in order to begin hypothesis testing as described above.

What is a null hypothesis?

In the case where the given statement is a well-established fact or default state of being in the real world, one can call it a null hypothesis (in the simpler word, nothing new). Well-established facts don’t need any hypothesis testing and hence can be called the null hypothesis. In cases, when there are any new claims made which is not well established in the real world, the null hypothesis can be thought of as the default state or opposite state of that claim. For example , in the previous section, the claim or hypothesis is made that the students studying for more than 6 hours a day gets more than 90% of marks in their examination. The null hypothesis, in this case, will be that the claim is not true or real. The null hypothesis can be stated that there is no relationship or association between the students reading more than 6 hours a day and they getting 90% of the marks. Any occurrence is only a chance occurrence. Another example of hypothesis is when somebody is alleged that they have performed a crime.

Null hypothesis is denoted by letter H with 0, e.g., [latex]H_0[/latex]

What is an alternate hypothesis?

When the given statement is a claim (unexpected event in the real world) and not yet proven, one can call/formulate it as an alternate hypothesis and accordingly define a null hypothesis which is the opposite state of the hypothesis. The alternate hypothesis is a new knowledge or truth that needs to be established. In simple words, the hypothesis or claim that needs to be tested against reality in the real world can be termed the alternate hypothesis. In order to reach a conclusion that the claim (alternate hypothesis) can be considered the new knowledge or truth (based on the available evidence), it would be important to reject the null hypothesis. It should be noted that null and alternate hypotheses are mutually exclusive and at the same time asymmetric. In the example given in the previous section, the claim that the students studying for more than 6 hours get more than 90% of marks can be termed as the alternate hypothesis.

Alternate hypothesis is denoted with H subscript a, e.g., [latex]H_a[/latex]

Once the hypothesis is formulated as null([latex]H_0[/latex]) and alternate hypothesis ([latex]H_a[/latex]), there are two possible outcomes that can happen from hypothesis testing. These outcomes are the following:

• Reject the null hypothesis : There is enough evidence based on which one can reject the null hypothesis. Let’s understand this with the help of an example provided earlier in this section. The null hypothesis is that there is no relationship between the students studying more than 6 hours a day and getting more than 90% marks. In a sample of 30 students studying more than 6 hours a day, it was found that they scored 91% marks. Given that the null hypothesis is true, this kind of hypothesis testing result will be highly unlikely. This kind of result can’t happen by chance. That would mean that the claim can be taken as the new truth or new knowledge in the real world. One can go and take further samples of 30 students to perform some more testing to validate the hypothesis. If similar results show up with other tests, it can be said with very high confidence that there is enough evidence to reject the null hypothesis that there is no relationship between the students studying more than 6 hours a day and getting more than 90% marks. In such cases, one can go to accept the claim as new truth that the students studying more than 6 hours a day get more than 90% marks. The hypothesis can be considered the new truth until the time that new tests provide evidence against this claim.
• Fail to reject the null hypothesis : There is not enough evidence-based on which one can reject the null hypothesis (well-established fact or reality). Thus, one would fail to reject the null hypothesis. In a sample of 30 students studying more than 6 hours a day, the students were found to score 75%. Given that the null hypothesis is true, this kind of result is fairly likely or expected. With the given sample, one can’t reject the null hypothesis that there is no relationship between the students studying more than 6 hours a day and getting more than 90% marks.

Examples of formulating the null and alternate hypothesis

The following are some examples of the null and alternate hypothesis.

Hypothesis Testing Steps

Here is the diagram which represents the workflow of Hypothesis Testing.

Figure 1. Hypothesis Testing Steps

Based on the above, the following are some of the  steps to be taken when doing hypothesis testing:

• State the hypothesis : First and foremost, the hypothesis needs to be stated. The hypothesis could either be the statement that is assumed to be true or the claim which is made to be true.
• Formulate the hypothesis : This step requires one to identify the Null and Alternate hypotheses or in simple words, formulate the hypothesis. Take an example of the canned sauce weighing 500 gm as the Null Hypothesis.
• Set the criteria for a decision : Identify test statistics that could be used to assess the Null Hypothesis. The test statistics with the above example would be the average weight of the sugar packet, and t-statistics would be used to determine the P-value. For different kinds of problems, different kinds of statistics including Z-statistics, T-statistics, F-statistics, etc can be used.
• Identify the level of significance (alpha) : Before starting the hypothesis testing, one would be required to set the significance level (also called as  alpha ) which represents the value for which a P-value less than or equal to  alpha  is considered statistically significant. Typical values of  alpha  are 0.1, 0.05, and 0.01. In case the P-value is evaluated as statistically significant, the null hypothesis is rejected. In case, the P-value is more than the  alpha  value, the null hypothesis is failed to be rejected.
• Compute the test statistics : Next step is to calculate the test statistics (z-test, t-test, f-test, etc) to determine the P-value. If the sample size is more than 30, it is recommended to use z-statistics. Otherwise, t-statistics could be used. In the current example where 20 packets of canned sauce is selected for hypothesis testing, t-statistics will be calculated for the mean value of 505 gm (sample mean). The t-statistics would then be calculated as the difference of 505 gm (sample mean) and the population means (500 gm) divided by the sample standard deviation divided by the square root of sample size (20).
• Calculate the P-value of the test statistics : Once the test statistics have been calculated, find the P-value using either of t-table or a z-table. P-value is the probability of obtaining a test statistic (t-score or z-score) equal to or more extreme than the result obtained from the sample data, given that the null hypothesis H0 is true.
• Compare P-value with the level of significance : The significance level is set as the allowable range within which if the value appears, one will be failed to reject the Null Hypothesis. This region is also called as Non-rejection region . The value of alpha is compared with the p-value. If the p-value is less than the significance level, the test is statistically significant and hence, the null hypothesis will be rejected.

P-Value: Key to Statistical Hypothesis Testing

Once you formulate the hypotheses, there is the need to test those hypotheses. Meaning, say that the null hypothesis is stated as the statement that housing price does not depend upon the average income of people staying in the locality, it would be required to be tested by taking samples of housing prices and, based on the test results, this Null hypothesis could either be rejected or failed to be rejected . In hypothesis testing, the following two are the outcomes:

• Reject the Null hypothesis
• Fail to Reject the Null hypothesis

Take the above example of the sugar packet weighing 500 gm. The Null hypothesis is set as the statement that the sugar packet weighs 500 gm. After taking a sample of 20 sugar packets and testing/taking its weight, it was found that the average weight of the sugar packets came to 495 gm. The test statistics (t-statistics) were calculated for this sample and the P-value was determined. Let’s say the P-value was found to be 15%. Assuming that the level of significance is selected to be 5%, the test statistic is not statistically significant (P-value > 5%) and thus, the null hypothesis fails to get rejected. Thus, one could safely conclude that the sugar packet does weigh 500 gm. However, if the average weight of canned sauce would have found to be 465 gm, this is way beyond/away from the mean value of 500 gm and one could have ended up rejecting the Null Hypothesis based on the P-value .

Hypothesis Testing for Problem Analysis & Solution Implementation

Hypothesis testing can be applied in both problem analysis and solution implementation. The following represents method on how you can apply hypothesis testing technique for both problem and solution space:

• Problem Analysis : Hypothesis testing is a systematic way to validate assumptions or educated guesses during problem analysis. It allows for a structured investigation into the nature of a problem and its potential root causes. In this process, a null hypothesis and an alternative hypothesis are usually defined. The null hypothesis generally asserts that no significant change or effect exists, while the alternative hypothesis posits the opposite. Through controlled experiments, data collection, or statistical analysis, these hypotheses are then tested to determine their validity. For example, if a software company notices a sudden increase in user churn rate, they might hypothesize that the recent update to their application is the root cause. The null hypothesis could be that the update has no effect on churn rate, while the alternative hypothesis would assert that the update significantly impacts the churn rate. By analyzing user behavior and feedback before and after the update, and perhaps running A/B tests where one user group has the update and another doesn’t, the company can test these hypotheses. If the alternative hypothesis is confirmed, the company can then focus on identifying specific issues in the update that may be causing the increased churn, thereby moving closer to a solution.
• Solution Implementation : Hypothesis testing can also be a valuable tool during the solution implementation phase, serving as a method to evaluate the effectiveness of proposed remedies. By setting up a specific hypothesis about the expected outcome of a solution, organizations can create targeted metrics and KPIs to measure success. For example, if a retail business is facing low customer retention rates, they might implement a loyalty program as a solution. The hypothesis could be that introducing a loyalty program will increase customer retention by at least 15% within six months. The null hypothesis would state that the loyalty program has no significant effect on retention rates. To test this, the company can compare retention metrics from before and after the program’s implementation, possibly even setting up control groups for more robust analysis. By applying statistical tests to this data, the company can determine whether their hypothesis is confirmed or refuted, thereby gauging the effectiveness of their solution and making data-driven decisions for future actions.
• Tests of Significance
• Hypothesis testing for the Mean
• z-statistics vs t-statistics (Khan Academy)

Hypothesis testing quiz

The claim that needs to be established is set as ____________, the outcome of hypothesis testing is _________.

Please select 2 correct answers

P-value is defined as the probability of obtaining the result as extreme given the null hypothesis is true

There is a claim that doing pranayama yoga results in reversing diabetes. which of the following is true about null hypothesis.

In this post, you learned about hypothesis testing and related nuances such as the null and alternate hypothesis formulation techniques, ways to go about doing hypothesis testing etc. In data science, one of the reasons why one needs to understand the concepts of hypothesis testing is the need to verify the relationship between the dependent (response) and independent (predictor) variables. One would, thus, need to understand the related concepts such as hypothesis formulation into null and alternate hypothesis, level of significance, test statistics calculation, P-value, etc. Given that the relationship between dependent and independent variables is a sort of hypothesis or claim , the null hypothesis could be set as the scenario where there is no relationship between dependent and independent variables.

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Shipping Your Product in Iterations: A Guide to Hypothesis Testing

Glancing at the App Store on any phone will reveal that most installed apps have had updates released within the last week. Software products today are shipped in iterations to validate assumptions and hypotheses about what makes the product experience better for users.

By Kumara Raghavendra

Kumara has successfully delivered high-impact products in various industries ranging from eCommerce, healthcare, travel, and ride-hailing.

PREVIOUSLY AT

A look at the App Store on any phone will reveal that most installed apps have had updates released within the last week. A website visit after a few weeks might show some changes in the layout, user experience, or copy.

Today, software is shipped in iterations to validate assumptions and the product hypothesis about what makes a better user experience. At any given time, companies like booking.com (where I worked before) run hundreds of A/B tests on their sites for this very purpose.

For applications delivered over the internet, there is no need to decide on the look of a product 12-18 months in advance, and then build and eventually ship it. Instead, it is perfectly practical to release small changes that deliver value to users as they are being implemented, removing the need to make assumptions about user preferences and ideal solutions—for every assumption and hypothesis can be validated by designing a test to isolate the effect of each change.

In addition to delivering continuous value through improvements, this approach allows a product team to gather continuous feedback from users and then course-correct as needed. Creating and testing hypotheses every couple of weeks is a cheaper and easier way to build a course-correcting and iterative approach to creating product value .

What Is Hypothesis Testing in Product Management?

While shipping a feature to users, it is imperative to validate assumptions about design and features in order to understand their impact in the real world.

This validation is traditionally done through product hypothesis testing , during which the experimenter outlines a hypothesis for a change and then defines success. For instance, if a data product manager at Amazon has a hypothesis that showing bigger product images will raise conversion rates, then success is defined by higher conversion rates.

One of the key aspects of hypothesis testing is the isolation of different variables in the product experience in order to be able to attribute success (or failure) to the changes made. So, if our Amazon product manager had a further hypothesis that showing customer reviews right next to product images would improve conversion, it would not be possible to test both hypotheses at the same time. Doing so would result in failure to properly attribute causes and effects; therefore, the two changes must be isolated and tested individually.

Thus, product decisions on features should be backed by hypothesis testing to validate the performance of features.

Different Types of Hypothesis Testing

A/b testing.

One of the most common use cases to achieve hypothesis validation is randomized A/B testing, in which a change or feature is released at random to one-half of users (A) and withheld from the other half (B). Returning to the hypothesis of bigger product images improving conversion on Amazon, one-half of users will be shown the change, while the other half will see the website as it was before. The conversion will then be measured for each group (A and B) and compared. In case of a significant uplift in conversion for the group shown bigger product images, the conclusion would be that the original hypothesis was correct, and the change can be rolled out to all users.

Multivariate Testing

Ideally, each variable should be isolated and tested separately so as to conclusively attribute changes. However, such a sequential approach to testing can be very slow, especially when there are several versions to test. To continue with the example, in the hypothesis that bigger product images lead to higher conversion rates on Amazon, “bigger” is subjective, and several versions of “bigger” (e.g., 1.1x, 1.3x, and 1.5x) might need to be tested.

Instead of testing such cases sequentially, a multivariate test can be adopted, in which users are not split in half but into multiple variants. For instance, four groups (A, B, C, D) are made up of 25% of users each, where A-group users will not see any change, whereas those in variants B, C, and D will see images bigger by 1.1x, 1.3x, and 1.5x, respectively. In this test, multiple variants are simultaneously tested against the current version of the product in order to identify the best variant.

Before/After Testing

Sometimes, it is not possible to split the users in half (or into multiple variants) as there might be network effects in place. For example, if the test involves determining whether one logic for formulating surge prices on Uber is better than another, the drivers cannot be divided into different variants, as the logic takes into account the demand and supply mismatch of the entire city. In such cases, a test will have to compare the effects before the change and after the change in order to arrive at a conclusion.

However, the constraint here is the inability to isolate the effects of seasonality and externality that can differently affect the test and control periods. Suppose a change to the logic that determines surge pricing on Uber is made at time t , such that logic A is used before and logic B is used after. While the effects before and after time t can be compared, there is no guarantee that the effects are solely due to the change in logic. There could have been a difference in demand or other factors between the two time periods that resulted in a difference between the two.

Time-based On/Off Testing

The downsides of before/after testing can be overcome to a large extent by deploying time-based on/off testing, in which the change is introduced to all users for a certain period of time, turned off for an equal period of time, and then repeated for a longer duration.

For example, in the Uber use case, the change can be shown to drivers on Monday, withdrawn on Tuesday, shown again on Wednesday, and so on.

While this method doesn’t fully remove the effects of seasonality and externality, it does reduce them significantly, making such tests more robust.

Test Design

Choosing the right test for the use case at hand is an essential step in validating a hypothesis in the quickest and most robust way. Once the choice is made, the details of the test design can be outlined.

The test design is simply a coherent outline of:

• The hypothesis to be tested: Showing users bigger product images will lead them to purchase more products.
• Success metrics for the test: Customer conversion
• Decision-making criteria for the test: The test validates the hypothesis that users in the variant show a higher conversion rate than those in the control group.
• Metrics that need to be instrumented to learn from the test: Customer conversion, clicks on product images

In the case of the product hypothesis example that bigger product images will lead to improved conversion on Amazon, the success metric is conversion and the decision criteria is an improvement in conversion.

After the right test is chosen and designed, and the success criteria and metrics are identified, the results must be analyzed. To do that, some statistical concepts are necessary.

When running tests, it is important to ensure that the two variants picked for the test (A and B) do not have a bias with respect to the success metric. For instance, if the variant that sees the bigger images already has a higher conversion than the variant that doesn’t see the change, then the test is biased and can lead to wrong conclusions.

In order to ensure no bias in sampling, one can observe the mean and variance for the success metric before the change is introduced.

Significance and Power

Once a difference between the two variants is observed, it is important to conclude that the change observed is an actual effect and not a random one. This can be done by computing the significance of the change in the success metric.

In layman’s terms, significance measures the frequency with which the test shows that bigger images lead to higher conversion when they actually don’t. Power measures the frequency with which the test tells us that bigger images lead to higher conversion when they actually do.

So, tests need to have a high value of power and a low value of significance for more accurate results.

While an in-depth exploration of the statistical concepts involved in product management hypothesis testing is out of scope here, the following actions are recommended to enhance knowledge on this front:

• Data analysts and data engineers are usually adept at identifying the right test designs and can guide product managers, so make sure to utilize their expertise early in the process.
• There are numerous online courses on hypothesis testing, A/B testing, and related statistical concepts, such as Udemy , Udacity , and Coursera .
• Using tools such as Google’s Firebase and Optimizely can make the process easier thanks to a large amount of out-of-the-box capabilities for running the right tests.

Using Hypothesis Testing for Successful Product Management

In order to continuously deliver value to users, it is imperative to test various hypotheses, for the purpose of which several types of product hypothesis testing can be employed. Each hypothesis needs to have an accompanying test design, as described above, in order to conclusively validate or invalidate it.

This approach helps to quantify the value delivered by new changes and features, bring focus to the most valuable features, and deliver incremental iterations.

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Understanding the basics

What is a product hypothesis.

A product hypothesis is an assumption that some improvement in the product will bring an increase in important metrics like revenue or product usage statistics.

What are the three required parts of a hypothesis?

The three required parts of a hypothesis are the assumption, the condition, and the prediction.

Why do we do A/B testing?

We do A/B testing to make sure that any improvement in the product increases our tracked metrics.

What is A/B testing used for?

A/B testing is used to check if our product improvements create the desired change in metrics.

What is A/B testing and multivariate testing?

A/B testing and multivariate testing are types of hypothesis testing. A/B testing checks how important metrics change with and without a single change in the product. Multivariate testing can track multiple variations of the same product improvement.

Kumara Raghavendra

Dubai, United Arab Emirates

Member since August 6, 2019

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Inside Design

5 steps to a hypothesis-driven design process

  •   mar 22, 2018.

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

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

Ready? Let’s dive in.

Step 1: Start with questions and assumptions

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

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

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

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

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

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

Step 2: Prioritize the questions and assumptions

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

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

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

Related: Go inside design at Google Ventures

Step 3: Turn them into hypotheses

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

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

Based on this question, the solutions can be:

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

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

From Lean UX

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

4. Develop an experiment and testing the hypothesis

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

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

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

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

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

5. Learn and build

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

“Design is a never-ending process.”

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

Principles from from The Lean Startup

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

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

by Sylvia Lai

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

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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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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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Value Hypothesis 101: A Product Manager's Guide

Talk to Sales

Humans make assumptions every day—it’s our brain’s way of making sense of the world around us, but assumptions are only valuable if they're verifiable . That’s where a value hypothesis comes in as your starting point.

A good hypothesis goes a step beyond an assumption. It’s a verifiable and validated guess based on the value your product brings to your real-life customers. When you verify your hypothesis, you confirm that the product has real-world value, thus you have a higher chance of product success. 

What Is a Verifiable Value Hypothesis?

A value hypothesis is an educated guess about the value proposition of your product. When you verify your hypothesis , you're using evidence to prove that your assumption is correct. A hypothesis is verifiable if it does not prove false through experimentation or is shown to have rational justification through data, experiments, observation, or tests. 

The most significant benefit of verifying a hypothesis is that it helps you avoid product failure and helps you build your product to your customers’ (and potential customers’) needs. 

Verifying your assumptions is all about collecting data. Without data obtained through experiments, observations, or tests, your hypothesis is unverifiable, and you can’t be sure there will be a market need for your product. 

A Verifiable Value Hypothesis Minimizes Risk and Saves Money

When you verify your hypothesis, you’re less likely to release a product that doesn’t meet customer expectations—a waste of your company’s resources. Harvard Business School explains that verifying a business hypothesis “...allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.” 

If you verify your hypothesis upfront, you’ll lower risk and have time to work out product issues. 

UserVoice Validation makes product validation accessible to everyone. Consider using its research feature to speed up your hypothesis verification process. 

Value Hypotheses vs. Growth Hypotheses 

Your value hypothesis focuses on the value of your product to customers. This type of hypothesis can apply to a product or company and is a building block of product-market fit . 

A growth hypothesis is a guess at how your business idea may develop in the long term based on how potential customers may find your product. It’s meant for estimating business model growth rather than individual products. 

Because your value hypothesis is really the foundation for your growth hypothesis, you should focus on value hypothesis tests first and complete growth hypothesis tests to estimate business growth as a whole once you have a viable product.

4 Tips to Create and Test a Verifiable Value Hypothesis

A verifiable hypothesis needs to be based on a logical structure, customer feedback data , and objective safeguards like creating a minimum viable product. Validating your value significantly reduces risk . You can prevent wasting money, time, and resources by verifying your hypothesis in early-stage development. 

A good value hypothesis utilizes a framework (like the template below), data, and checks/balances to avoid bias. 

1. Use a Template to Structure Your Value Hypothesis 

By using a template structure, you can create an educated guess that includes the most important elements of a hypothesis—the who, what, where, when, and why. If you don’t structure your hypothesis correctly, you may only end up with a flimsy or leap-of-faith assumption that you can’t verify. 

A true hypothesis uses a few guesses about your product and organizes them so that you can verify or falsify your assumptions. Using a template to structure your hypothesis can ensure that you’re not missing the specifics.

You can’t just throw a hypothesis together and think it will answer the question of whether your product is valuable or not. If you do, you could end up with faulty data informed by bias , a skewed significance level from polling the wrong people, or only a vague idea of what your customer would actually pay for your product. 

A template will help keep your hypothesis on track by standardizing the structure of the hypothesis so that each new hypothesis always includes the specifics of your client personas, the cost of your product, and client or customer pain points. 

A value hypothesis template might look like: 

[Client] will spend [cost] to purchase and use our [title of product/service] to solve their [specific problem] OR help them overcome [specific obstacle]. 

An example of your hypothesis might look like: 

B2B startups will spend $500/mo to purchase our resource planning software to solve resource over-allocation and employee burnout.

By organizing your ideas and the important elements (who, what, where, when, and why), you can come up with a hypothesis that actually answers the question of whether your product is useful and valuable to your ideal customer. 

2. Turn Customer Feedback into Data to Support Your Hypothesis  

Once you have your hypothesis, it’s time to figure out whether it’s true—or, more accurately, prove that it’s valid. Since a hypothesis is never considered “100% proven,” it’s referred to as either valid or invalid based on the information you discover in your experiments or tests. Additionally, your results could lead to an alternative hypothesis, which is helpful in refining your core idea.

To support value hypothesis testing, you need data. To do that, you'll want to collect customer feedback . A customer feedback management tool can also make it easier for your team to access the feedback and create strategies to implement or improve customer concerns. 

If you find that potential clients are not expressing pain points that could be solved with your product or you’re not seeing an interest in the features you hope to add, you can adjust your hypothesis and absorb a lower risk. Because you didn’t invest a lot of time and money into creating the product yet, you should have more resources to put toward the product once you work out the kinks. 

On the other hand, if you find that customers are requesting features your product offers or pain points your product could solve, then you can move forward with product development, confident that your future customers will value (and spend money on) the product you’re creating. 

A customer feedback management tool like UserVoice can empower you to challenge assumptions from your colleagues (often based on anecdotal information) which find their way into team decision making . Having data to reevaluate an assumption helps with prioritization, and it confirms that you’re focusing on the right things as an organization.

3. Validate Your Product 

Since you have a clear idea of who your ideal customer is at this point and have verified their need for your product, it’s time to validate your product and decide if it’s better than your competitors’. 

At this point, simply asking your customers if they would buy your product (or spend more on your product) instead of a competitor’s isn’t enough confirmation that you should move forward, and customers may be biased or reluctant to provide critical feedback. 

Instead, create a minimum viable product (MVP). An MVP is a working, bare-bones version of the product that you can test out without risking your whole budget. Hypothesis testing with an MVP simulates the product experience for customers and, based on their actions and usage, validates that the full product will generate revenue and be successful.  

If you take the steps to first verify and then validate your hypothesis using data, your product is more likely to do well. Your focus will be on the aspect that matters most—whether your customer actually wants and would invest money in purchasing the product.

4. Use Safeguards to Remain Objective 

One of the pitfalls of believing in your product and attempting to validate it is that you’re subject to confirmation bias . Because you want your product to succeed, you may pay more attention to the answers in the collected data that affirm the value of your product and gloss over the information that may lead you to conclude that your hypothesis is actually false. Confirmation bias could easily cloud your vision or skew your metrics without you even realizing it. 

Since it’s hard to know when you’re engaging in confirmation bias, it’s good to have safeguards in place to keep you in check and aligned with the purpose of objectively evaluating your value hypothesis. 

Safeguards include sharing your findings with third-party experts or simply putting yourself in the customer’s shoes.

Third-party experts are the business version of seeking a peer review. External parties don’t stand to benefit from the outcome of your verification and validation process, so your work is verified and validated objectively. You gain the benefit of knowing whether your hypothesis is valid in the eyes of the people who aren’t stakeholders without the risk of confirmation bias. 

In addition to seeking out objective minds, look into potential counter-arguments , such as customer objections (explicit or imagined). What might your customer think about investing the time to learn how to use your product? Will they think the value is commensurate with the monetary cost of the product? 

When running an experiment on validating your hypothesis, it’s important not to elevate the importance of your beliefs over the objective data you collect. While it can be exciting to push for the validity of your idea, it can lead to false assumptions and the permission of weak evidence. 

Validation Is the Key to Product Success

With your new value hypothesis in hand, you can confidently move forward, knowing that there’s a true need, desire, and market for your product.

Because you’ve verified and validated your guesses, there’s less of a chance that you’re wrong about the value of your product, and there are fewer financial and resource risks for your company. With this strong foundation and the new information you’ve uncovered about your customers, you can add even more value to your product or use it to make more products that fit the market and user needs. 

If you think customer feedback management software would be useful in your hypothesis validation process, consider opting into our free trial to see how UserVoice can help.

Heather Tipton

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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.

• Collecting Data

Frequently Asked Questions

A hypothesis is a tentative statement about the relationship between two or more  variables. It is a specific, testable prediction about what you expect to happen in a study.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore a number of factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk wisdom that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis.   In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in a number of different ways. One of the basic principles of any type of scientific research is that the results must be replicable.   By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. How would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests that there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative sample of the population and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• Complex hypothesis: "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
• "There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

• "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
• "Adults will perform better on a memory task than children." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when it would be impossible or difficult to  conduct an experiment . These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can then be used to look at how the variables are related. This type of research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

A Word From Verywell

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Some examples of how to write a hypothesis include:

• "Staying up late will lead to worse test performance the next day."
• "People who consume one apple each day will visit the doctor fewer times each year."
• "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

• The research question
• The independent variable (IV)
• The dependent variable (DV)
• The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Tips to Create and Test a Value Hypothesis: A Step-by-Step Guide

Developing a robust value hypothesis is crucial as you bring a new product to market, guiding your startup toward answering a genuine market need. Constructing a verifiable value hypothesis anchors your product's development process in customer feedback and data-driven insight rather than assumptions.

This framework enables you to clarify the potential value your product offers and provides a foundation for testing and refining your approach, significantly reducing the risk of misalignment with your target market. To set the stage for success, employ logical structures and objective measures, such as creating a minimum viable product, to effectively validate your product's value proposition.

What Is a Verifiable Value Hypothesis?

A verifiable value hypothesis articulates your belief about how your product will deliver value to customers. It is a testable prediction aimed at demonstrating the expected outcomes for your target market.

To ensure that your value hypothesis is verifiable, it should adhere to the following conditions:

• Specific : Clearly defines the value proposition and the customer segment.
• Measurable : Includes metrics by which you can assess success or failure.
• Achievable : Realistic based on your resources and market conditions.
• Relevant : Directly addresses a significant customer need or desire.
• Time-Bound : Has a defined period for testing and validation.

When you create a value hypothesis, you're essentially forming the backbone of your business model. It goes beyond a mere assumption and relies on customer feedback data to inform its development. You also safeguard it with objective measures, such as a minimum viable product, to test the hypothesis in real life.

By articulating and examining a verifiable value hypothesis, you understand your product's potential impact and reduce the risk associated with new product development. It's about making informed decisions that increase your confidence in the product's potential success before committing significant resources.

Value Hypotheses vs. Growth Hypotheses

Value hypotheses and growth hypotheses are two distinct concepts often used in business, especially in the context of startups and product development.

Value Hypotheses : A value hypothesis is centered around the product itself. It focuses on whether the product truly delivers customer value. Key questions include whether the product meets a real need, how it compares to alternatives, and if customers are willing to pay for it. Valuing a value hypothesis is crucial before a business scales its operations.

Growth Hypotheses : A growth hypothesis, on the other hand, deals with the scalability and marketing aspects of the business. It involves strategies and channels used to acquire new customers. The focus is on how to grow the customer base, the cost-effectiveness of growth strategies, and the sustainability of growth. Validating a growth hypothesis is typically the next step after confirming that the product has value to the customers.

In practice, both hypotheses are crucial for the success of a business. A value hypothesis ensures the product is desirable and needed, while a growth hypothesis ensures that the product can reach a larger market effectively.

Tips to Create and Test a Verifiable Value Hypothesis

Creating a value hypothesis is crucial for understanding what drives customer interest in your product. It's an educated guess that requires rigor to define and clarity to test. When developing a value hypothesis, you're attempting to validate assumptions about your product's value to customers. Here are concise tips to help you with this process:

1. Understanding Your Market and Customers

Before formulating a hypothesis, you need a deep understanding of your market and potential customers. You're looking to uncover their pain points and needs which your product aims to address.

Begin with thorough market research and collect customer feedback to ensure your idea is built upon a solid foundation of real-world insights. This understanding is pivotal as it sets the tone for a relevant and testable hypothesis.

• Define Your Value Proposition Clearly: Articulate your product's value to the user. What problem does it solve? How does it improve the user's life or work?
• Identify Your Target Audience. Determine who your ideal customers are. Understand their needs, pain points, and how they currently address the problem your product intends to solve.

2. Defining Clear Assumptions

The next step is to outline clear assumptions based on your idea that you believe will bring value to your customers. Each assumption should be an assertion that directly relates to how your customers will find your product valuable.

For example, if your product is a task management app, you might assume that the ability to share task lists with team members is a pain point for your potential customers. Remember, assumptions are not facts—they are educated guesses that need verification.

3. Identify Key Metrics for Your Hypothesis Test

Once you've defined your assumptions, delineate the framework for testing your value hypothesis. This involves designing experiments that validate or invalidate your assumptions with measurable outcomes. Ensure that your hypothesis can be tested with measurable outcomes. This could be in the form of user engagement metrics, conversion rates, or customer satisfaction scores.

Determine what success looks like and define objective metrics that will prove your product's value. This could be user engagement, conversion rates, or revenue. Choosing the right metrics is essential for an accurate test. For instance, in your test, you might measure the increase in customer retention or the decrease in time spent on task organization with your app. Construct your test so that the results are unequivocal and actionable.

4. Construct a Testable Proposition

Formulate your hypothesis in a way that can be tested empirically. Use qualitative research methods such as interviews, surveys, and observation to gather data about your potential users. Formulate your value hypothesis based on insights from this research. Plan experiments that can validate or invalidate your value hypothesis. This might involve A/B testing, user testing sessions, or pilot programs.

A good example is to posit that "Introducing feature X will increase user onboarding by Y%." Avoid complexity by testing one variable simultaneously. This helps you identify which changes are actually making a difference.

5. Applying Evidence to Innovation

When your data indicates a promising avenue for product development , it's imperative that you validate your growth hypothesis through experimentation. Align your value proposition with the evidence at hand.

Develop a simplified version of your product that allows you to test the core value proposition with real users without investing in full-scale production. Start by crafting a minimum viable product ( MVP ) to begin testing in the market. This approach helps mitigate risk by not investing heavily in unproven ideas. Use analytics tools to collect data on how users interact with your MVP. Look for patterns that either support or contradict your value hypothesis.

If the data suggests that your value hypothesis is wrong, be prepared to revise your hypothesis or pivot your product strategy accordingly.

6. Gather Customer Feedback

Integrating customer feedback into your product development process can create a more tailored value proposition. This step is crucial in refining your product to meet user needs and validate your hypotheses.

Use customer feedback tools to collect data on how users interact with your MVP. Look for patterns that either support or contradict your value hypothesis. Here are some ways to collect feedback effectively :

• Feedback portals
• User testing sessions
• In-app feedback
• Website widgets
• Direct interviews
• Focus groups
• Feedback forums

Create a centralized place for product feedback to keep track of different types of customer feedback and improve SaaS products while listening to their customers. Rapidr helps companies be more customer-centric by consolidating feedback across different apps, prioritizing requests, having a discourse with customers, and closing the feedback loop.

7. Analyze and Iterate Quickly

Review the data and analyze customer feedback to see if it supports your hypothesis. If your hypothesis is not supported, iterate on your assumptions, and test again. Keep a detailed record of your hypotheses, experiments, and findings. This documentation will help you understand the evolution of your product and guide future decision-making.

Use the feedback and data from your tests to make quick iterations of your product and drive product development . This allows you to refine your value proposition and improve the fit with your target audience. Engage with your users throughout the process. Real-world feedback is invaluable and can provide insights that data alone cannot.

• Identify Patterns : What commonalities are present in the feedback?
• Implement Changes : Prioritize and make adjustments based on customer insights.

9. Align with Business Goals and Stay Customer-Focused

Ensure that your value hypothesis aligns with the broader goals of your business. The value provided should ultimately contribute to the success of the company. Remember that the ultimate goal of your value hypothesis is to deliver something that customers find valuable. Maintain a strong focus on customer needs and satisfaction throughout the process.

10. Communicate with Stakeholders and Update them

Keep all stakeholders informed about your findings and the implications for the product. Clear communication helps ensure everyone is aligned and understands the rationale behind product decisions. Communicate and close the feedback loop with the help of a product changelog through which you can ​​announce new changes and engage with customers.

Understanding and validating a value hypothesis is essential for any business, particularly startups. It involves deeply exploring whether a product or service meets customer needs and offers real value. This process ensures that resources are invested in desirable and useful products, and it's a critical step before considering scalability and growth.

By focusing on the value hypothesis, businesses can better align their offerings with market demand, leading to more sustainable success. Placing customer feedback at the center of the process of testing a value hypothesis helps you develop a product that meets your customers' needs and stands out in the market.

Rapidr helps companies be more customer-centric by consolidating feedback across different apps, prioritizing requests, having a discourse with customers, and closing the feedback loop.

Build better products with user feedback

Rapidr helps SaaS companies understand what customers need through feedback, prioritize what to build next, inform the roadmap, and notify customers on product releases

Hypothesis validation

• May 24, 2022
• Product Ownership
• user stories

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Table of Contents

Hi everyone!!

This article is based on the scenario presented in our article ‘ User Story Splitting ‘. If you didn’t have the chance yet to take a look at it, we encourage you to do it before continue with this article’s reading. We just want to be sure that you are not missing context but, just as a quick recap…

• …we are working in the product department of a company called ‘Frame Store’. We sell frames …
• … what we used to get from our business people is a big idea, inspirational ( sometimes not 100% realistic and rational! ) of a very nice and cool feature …

And… these are the assumptions we would like to validate:

• If the users are able to customize their menu, they will be able to find the options easily, improving performance and satisfaction.
• If the users are able to search menu options, they will be able to find the options easily, improving performance and satisfaction.

Validating these hypotheses we are seeking to reduce the risk of failure and to be sure we are giving real value to the user.

Let’s jump to the next sections where you will discover in more details how you can reduce the risks, this is ‘as simple as’ follow a hypothesis-driven validation or, what is the same, work on the hypothesis (with empiric data) before to take any decision and move forward.

Identify your assumptions

For our example, we have the following assumptions:

We need to ask ourselves some questions to be sure they are feasible , desirables and viable .

Some examples of these questions could be…

• Are we facing some technological challenges that could make these hypotheses not realistic?
• Are we trying to solve a real pain point for our users?
• Does make sense for our company to improve that part of the process right now?

With this 3 questions we can have a very high level idea if our hypotheses is feasible, desirable and viable. We could iterate more in each of them, trying to go more deep on any of these characteristics. This is just a good point to start.

Reframe assumptions as “hypotheses”

This step could seems unnecessary but it’s really required to change the way we are visioning the ideas in our minds ( and how we share them with others).

Assumptions are something we take from granted, it is what it is. On the other hand, hypotheses are like a tentative assumption. We are implicitly saying that we have not proof of the certainty of the assumption.

• We believe that , if the users are able to customize their menu, they will be able to find them easily, improving performance and satisfaction.
• We believe that , if the users are able to search menu options, they will be able to find them easily, improving performance and satisfaction.

A good exercise in here is also the create ‘null hypotheses’, like:

• We believe that, if the users are able to customize their menu, they will be able to find the options in the same way than before, not improving performance and satisfaction.
• We believe that, if the users are able to search menu options, they will be able to find the options in the same way than before, not improving performance and satisfaction.

These are the ones we want to prove false.

Rank them in order of importance

One good advice in order to prioritize hypothesis is to evaluate what could happen if the hypothesis is proven false. In another words, what’s the Cost of Delay ( a concept some of you already know from Safe methodology).

What would be the impact for our app, or even higher level, for our company!

Design appropriate tests

As a good advice I would say ‘ test first what has more risk ‘. This could be apply to anything, do not start with the easy part just to realize that the more complex one is impossible to achieve. Let’s discard it ASAP.

There are multiple ways to perform a test on the hypothesis (Qualitative and Quantitative) … we will go through few of them:

Implement a minimum valuable product with the hypotheses we need to validate. We don’t need to have the full functionality implemented, just the most important features.

If we want to validate our hypothesis ‘ We believe that, if the users are able to search menu options, they will be able to find them easily, improving performance and satisfaction. ‘, let’s implement a search field but that’s everything, nothing else, nothing fancy.

Let’s release it to half of our users and see how it goes. Do not spend a lot of time in something that maybe nobody want. Once we proof that our users likes it and is giving value, we will work to improve it!

This will require even less implementation than the AB testing of the feature BUT could generate more frustration to our users. To validate our hypothesis we will…

• Add the search field to our landing page
• Let the users write the information they need to search
• Do not perform any search, the portal could prompt a message like ‘Sorry, this feature is not still available, we are working on it!’.
• Send to our analytics system how many people is actually using the feature.

As I said before, I believe that this kind of communication need to be handle appropriately to reduce frustration from the users ( some companies uses messages like ‘sorry, this feature is not available in your country yet’… a kind of white lie but could be less harming ).

This validation process could be executed inside an AB test, just replacing the MVP implementation with this Fate Door.

Let’s ask to our users and see what’s the outcome. What is important in here is to not ask direct questions where we are driving the users to the answer we expect is the correct one.

During all this years, I heard like a hundred times ‘remember the yellow Walkman!’ … how can I forget about it!! That example teach us what we don’t have to ask but, the real value is on ‘what do we have to ask?’. It took me few days to come with just few ‘none interfering’ questions!

As advice, try to formulate questions following :

• Do you believe that …
• How do you feel when…

Try to avoid yes/no answers. I would always prefer to go with a scale from ‘less likely’ to ‘very likely’ or similar.

Take in consideration that our product needs to be designed and implemented considering hypotheses validation since day 1. We can not delay the integration of an AB test framework to the last one of our implementations, this needs to be prioritize high!

Conduct the tests

You can user tools like Firebase to perform your AB. Let’s place in there the segmentation of your users you want to move to the test and wait until the data you receive is significative enough ( some tools like Firebase tells it itself).

How long will take? Depends on the traffic you have on that part of the portal/app. Changes on the landing page will give you significative data sooner that changes on some detailed page, like users profiles or any other with less traffic.

About surveys, pick well your candidates to have a realistic representation of your users. Imagine that more than 75% of our users in the Frame Store Portal are people older than 50 years. Our candidates need to represents this distribution so, do not pick a lot of 20-30 years old candidates!

Synthesize your learnings

We need to be openminded at this stage of the hypothesis validation. Maybe, the hypothesis is proved false. This should never be consider a mistake! this is a success ! We where able to save time and money for the company stopping of pursuing something that has no value.

A lot of companies failed because they were unable to validate hypotheses but not just that. I saw cases where, after designed and conducted the appropriate test, they failed reading the results. How could that be possible?? that is just because people is too committed to the assumption that they do not accept that it could be proved false. People sold the idea to upper management before prove it right and now,… ‘ Wow… I can not go back and tell them that what I thought was not true, that I was wrong! ’. This is a big mistake, the biggest one we can commit at this point. We spend time and money to validate an hypothesis and now, we will spend time implementing it even if it not useful for anybody!. Crazy stuff…

Be openminded, change your thought to ‘ Wow… luckily we proved that our assumption where false before move forward with it. Now we know more about our users needs! we can formulate new assumptions! ’. Nobody failed!

Implementation with the certainty of giving value to your users is the best feeling ever! you listen to them and work with the team ( UX, developers, QAs,… ) to make thinks real!

Is there a small chance of failure ( will be almost impossible to reduce the risk to 0! ) but nothing if you compare it with the changes of failure when you put your team to work implementing an assumption that you didn’t validate!

Conclusions

Hope this article gives you some clues to improve your hypothesis validation process. Our advice…validate, validate and … iterate!

We wish you all the best on this long but very fulfilling process!

Product owner with a background of software developer.

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• PMC9882446.1 ; 2023 Jan 18
• ➤ PMC9882446.2; 2023 May 26

This is a preprint.

Development, validation, and usage of metrics to evaluate the quality of clinical research hypotheses.

1 College of Behavioral, Social, and Health Sciences, Clemson University, Clemson, South Carolina, USA;

Yuchun Zhou

2 Patton College of Education, Ohio University, Athens, Ohio, USA;

James J. Cimino

3 Informatics Institute, School of Medicine, University of Alabama, Birmingham, Alabama, USA;

Jay H. Shubrook

4 College of Osteopathic Medicine, Touro University, Vallejo, California, USA;

Vimla L. Patel

5 The New York Academy of Medicine, New York, New York, USA;

Sonsoles De Lacalle

6 College of Art and Science, California State University Channel Islands, Camarillo, California, USA;

Aneesa Weaver

7 Russ College of Engineering and Technology, Ohio University, Athens, Ohio, USA

Associated Data

Objectives:.

Metrics and instruments can provide guidance for clinical researchers to assess their potential research projects at an early stage before significant investment. Furthermore, metrics can also provide structured criteria for peer reviewers to assess others’ clinical research manuscripts or grant proposals. This study aimed to develop, test, validate, and use evaluation metrics and instruments to accurately, consistently, and conveniently assess the quality of scientific hypotheses for clinical research projects.

Materials and Methods:

Metrics development went through iterative stages, including literature review, metrics and instrument development, internal and external testing and validation, and continuous revisions in each stage based on feedback. Furthermore, two experiments were conducted to determine brief and comprehensive versions of the instrument.

The brief version of the instrument contained three dimensions: validity, significance, and feasibility. The comprehensive version of metrics included novelty, clinical relevance, potential benefits and risks, ethicality, testability, clarity, interestingness, and the three dimensions of the brief version. Each evaluation dimension included 2 to 5 subitems to evaluate the specific aspects of each dimension. For example, validity included clinical validity and scientific validity. The brief and comprehensive versions of the instruments included 12 and 39 subitems, respectively. Each subitem used a 5-point Likert scale.

Conclusion:

The validated brief and comprehensive versions of metrics can provide standardized, consistent, and generic measurements for clinical research hypotheses, allow clinical researchers to prioritize their research ideas systematically, objectively, and consistently, and can be used as a tool for quality assessment during the peer review process.

Introduction

A hypothesis is an educated guess or statement about the relationship between two or more variables [ 1 , 2 ]. The hypothesis generation process is critical and decisive in determining the significance of a clinical research project or scientific project. Although much progress has been achieved in scientific thinking, reasoning, and analogy [ 3 – 8 ], which are critical skills in hypothesis generation, knowledge about the scientific hypothesis generation process, including how to facilitate the process, especially in a clinical research context, is limited. Many data science researchers believe that secondary data analytic tools can facilitate hypothesis generation [ 9 ]. Nevertheless, there is a lack of studies demonstrating the role of a secondary data analysis tool in this process in clinical research. We developed a visual interactive analytic tool for filtering and summarizing large health data sets coded with hierarchical terminologies (VIADS, https://www.viads.info [ 10 ]) to filter, compare, summarize, and visualize datasets coded with hierarchical terminologies (e.g., International Classification of Diseases, 9 th Revision, Clinical Modification, ICD-9-CM). VIADS can also assist clinical researchers with generating hypotheses. Visual examples of VIADS include hierarchical graphs, bar charts, and 3D plots. Users can obtain expanded information via interactive features, change graph layouts (e.g., small, medium, and large horizontal spacing), zoom in and out, and move, save, and export graphs and their data files.

To put this manuscript in the appropriate context, we provide some background information on the entire project and how we conducted it to elaborate on how it fits the bigger picture. To explore the clinical researchers’ hypothesis generation processes, we conducted one-on-one study sessions in which researchers (i.e., participants) generated hypotheses using the same datasets within two hours with or without VIADS [ 11 ]. This was a 2 × 2 study design (with and without VIADS by experienced and inexperienced clinical researchers). The quality of each scientific hypothesis generated by the participants in the study [ 12 , 13 ] was assessed by an expert panel using the same metrics. The aggregated quality assessment results, along with the number of hypotheses and the average time used to generate a hypothesis, were used to detect the differences in the hypotheses generated by the participants [ 12 ]. A reliable, generic, and convenient tool is required to have a reliable, consistent, and accurate assessment of the quality of the generated scientific hypotheses [ 14 ].

The original purpose of developing metrics is to evaluate the hypotheses generated by the participants in our research project. Furthermore, the validated metrics and instruments can be very useful in clinical research. Researchers can use the instruments to compare and select more valuable and impactful hypotheses to pursue in their research endeavor at an early stage before any significant investment in resources. Furthermore, the instruments can be used during peer review processes for clinical research manuscripts or grant proposals. Traditionally, the peer review process is conducted by human experts, which can be a subjective assessment. Using an explicit, clearly defined, consistent, and comprehensive assessment tool based on metrics can provide a solid foundation for a relatively more objective, consistent, and perhaps more accurate evaluation during the peer review process of clinical research projects. The lack of a significant, meaningful, and impactful hypothesis to start with can make all other aspects of the research projects meaningless, regardless of rigor or validity. Therefore, the development and validation of such metrics play an important role in facilitating the launch of a more impactful research project and conducting a more objective, consistent, and accurate peer-review evaluation. In this manuscript, we introduce the approach we used to develop and validate the metrics, the results of the metrics and instruments, and the preliminary experience of the usage of the metrics. We hope to share the metrics and instruments as potential tools and the methodology we used to develop them with the clinical research community.

Materials and Methods

Study flow and internal validation.

The development of the metrics went through a series of iterative stages ( Figure 1 ) [ 15 – 17 ]. One author (XJ, a medical informatics researcher) reviewed the literature and drafted the metrics. Then, two authors (XJ and YCZ, a research methodologist) discussed the outlined metrics, formulated the initial metrics, and revised the metrics after all confusion and concerns were addressed. This was the first internal validation level between two team members. The adjusted metrics were distributed to the research team as anonymous surveys about the evaluation items for feedback. This step was conducted in three rounds to incorporate all the feedback received. This step constituted the second level of internal validation among the entire team.

Development, validation, and usage of the metrics to assess the quality of clinical research hypotheses. Blue arrows indicate the development stages of metrics; solid green arrows indicate the feedback incorporated into the metrics from each stage; green hollowed arrow indicates future work

External validation

After completing the internal validation, an iterative external validation process was conducted by engaging additional four invited clinical research experts. The criteria to be eligible as a clinical research expert were pre-defined during the design of the research project (please refer to our prior publication for details [ 11 ]). The instrument used in the initial external validation is shown in Appendix 1 . The internal validation processes on the instrument (i.e., the evaluation dimensions, subitems, and scales of subitems) followed a revised Delphi method [ 18 – 22 ], which included transparent and open discussions (via face-to-face meetings, emails, and complementary video conferences) among the research team.

The external validation consisted of three steps, (1) initial validation of the metrics, (2) experimental evaluations by using the metrics to assess hypotheses generated during the study sessions, and (3) refinement based on the feedback and results of the experimental evaluations ( Figure 2 ). A survey ( Appendix 2 ) that served as the medium validation instrument was used among all expert panel members (including three senior consultants from the research team and four external clinical research experts) to obtain feedback, which was incorporated into the final metrics ( Table 1 and Appendix 3 ). A 10-item evaluation instrument was formulated from the development and validation processes.

Refinement process of the clinical research hypotheses quality evaluation instrument

Evaluation items and subitems in the metrics used to assess the scientific hypotheses in clinical research

Note: Validity, significance, and feasibility, denoted in the green background, were used in the brief version of the instrument to conduct gateway evaluations for all the hypotheses generated in the study.

Experimental evaluation 1

In experimental evaluation 1, we performed validation analysis for the ten evaluation items (without subitems) using 19 hypotheses generated via pilot studies of the research project. These hypotheses were randomly assigned to two Qualtrics surveys (10 and 9 hypotheses). The seven members are our evaluation team (i.e., an expert panel), all of whom with a medical or methodology background. They rated all the hypotheses. The inter-rater agreement of the seven experts’ ratings on the 19 hypotheses was analyzed using the intra-class correlation (ICC). We used descriptive statistics to analyze the results of the survey. Based on the mean results (i.e., the average rating scores for each hypothesis) from experimental evaluation 1, we identified the best and worst examples of hypotheses, which were used in experimental evaluation 2 to provide examples for the expert panel members to better calibrate their rating scores in the assessment of the remaining hypotheses.

Experimental evaluation 2

Experimental evaluation 2 included 30 randomly selected hypotheses from the study sessions using the 10-item evaluation instrument ( Figure 3 ). In the instructions, we provided the best and worst examples of hypotheses based on the experimental evaluation 1 results and set a screening item: validity. If a statement is not a hypothesis, further evaluation is unnecessary. If three or more experts scored at 1 (lowest rating) in validity for any of the hypotheses, it was removed from the following analysis. ICC analysis was performed to examine the consistency of the seven experts’ ratings on the valid hypotheses using the ten items. The evaluation results were compared using a paired t -test analysis.

Ten-item evaluation instrument for clinical research hypothesis screening and evaluation

Instruments used for validation and experiment evaluations

All steps mentioned above (initial draft development, internal validation, external validation, refinement, and revisions in between the steps) were conducted iteratively using quantitative and qualitative approaches (e.g., Qualtrics surveys, emails, additional phone calls, and virtual conferences). The evaluations of the instrument (with 10 items and 39 subitems), i.e., the validation process before experts used the instrument to conduct the experimental evaluations, including a 5-point Likert scale and three additional options of unable to assess, unnecessary subitem, or use this item only ( Appendix 2 ). The evaluation instrument (with 10 items) used in experimental evaluations 1 and 2 included a 5-point Likert scale and an option of not applicable ( Figure 3 ). The gateway evaluation and the results are published separately. This study was approved by the Ohio University Institutional Review Board (18-X-192) and Clemson University Institutional Review Board (IRB2020–056).

We present comprehensive (10 items and 39 subitems, Appendix 3 ) and brief versions (3 items, 12 subitems, Table 1 , Appendix 4 ) of the instrument to assess the quality of clinical research hypotheses and the evidence generated from experimental evaluations. Most measurements for evaluating the quality of clinical research hypotheses from the literature [ 1 , 2 , 9 , 23 – 33 ] include the following ten dimensions: validity , significance , novelty , clinical relevance , potential benefits and risks , ethicality , feasibility , testability , clarity , and researcher interest level . We developed 39 sub-items to measure each dimension comprehensively and unambiguously ( Table 1 ). The quality of each item was measured using a 5-point Likert scale. Table 1 shows all the evaluation items and subitems and how they were used to evaluate the quality of clinical research hypotheses. Table 2 presents two examples of hypotheses and their quality evaluation results among all evaluators when using the 3-item instrument ( Appendix 4 ).

Example of hypotheses and evaluation results using the 3-item instrument

Note: Hypothesis 1 : Patients who have hypertension between 2005 and 2015, do hypertension patients have a higher obesity morbidity rate (ICD9 codes: 27801) in 2015 than in 2005? Hypothesis 2 : Whether the changes in packed food consumption caused an increase in diabetes (ICD9 code: #250) from 2005 (case counts: 774) to 2015 (case counts: 1281) at the zip code level? R1: reviewer 1; NA: not applicable (i.e., an evaluator cannot assess the item, a hypothesis is invalid, and all following items are not evaluated).

In experimental evaluation 1, the experts’ evaluation scores for the 19 hypotheses across the ten criteria were averaged, and none of the ten criteria could achieve a moderate ICC coefficient (>0.50). Therefore, experimental evaluation 2 was conducted, validity was set as a screening item, and one best and one not-so-good examples of hypotheses from experimental evaluation 1 were provided in the instructions of experimental evaluation 2.

In the experimental evaluation 2 result analysis, the results of the screening item were checked first. The valid sample size included 17 hypotheses (out of 30) in experimental evaluation 2. Then, the inter-rater agreement of the 17 hypotheses was checked using ICC analyses. Half the ten criteria achieved a moderate ICC value (0.50–0.75). Based on the ICC results and qualitative evaluation of the ten criteria, a decision was made to retain three measures (i.e., validity, significance, and feasibility) for a shortened version of the evaluation instrument.

The paired t -test indicated no significant difference (t = 1.74, p = .13) between the ratings using the 3-item ( Appendix 4 ) and 10-item instruments. Figure 3 shows the 10-item evaluation instrument used for experimental evaluation 2, including the best and worst examples. Figure 4 presents the steps used in this study and the corresponding results to provide a summary view of the methods and results.

Summary of methods, steps, and corresponding results of development and validation of metrics in assessing the quality of clinical research hypotheses

Hypothesis generation is a highly sophisticated cognitive process. Not all information used during the processes is a conscious or explicit choice. Our study explored the process of scientific hypothesis generation using the same clinical datasets to determine whether a secondary data analytic tool could facilitate the process. Establishing the evaluation metrics was the first step and was the critical foundation for the overall study and understanding of the entire process. Comprehensive and objective measures were given more weight during the development of the metrics. In our studies, the clinical researchers generated a few to over a dozen hypotheses within two hours [ 12 , 13 ]. However, not all hypotheses were of high quality. Therefore, it was not conducive to using the experts’ time to comprehensively evaluate each hypothesis generated during the study sessions.

Furthermore, using the entire set of metrics, including all items and subitems, to evaluate each generated hypothesis may be unnecessary. Thus, we used “gateway” evaluations as a filter to identify the higher-quality hypotheses. The experts can determine the higher-quality hypotheses more carefully, thoroughly, and comprehensively during the comprehensive evaluation. Therefore, validity was used as a screening item, and the “not a hypothesis” option was added in the initial assessment, enlightened by the experimental evaluation 1 results.

The results of experimental evaluation 2 aided in determining a brief evaluation instrument with the 3 items used to evaluate the rest of the hypotheses generated by the participants during the gateway evaluation ( Figures 1 and ​ and2). 2 ). From the ICC analysis in experimental evaluation 2, feasibility, testability, and clarity have the highest ICC values among the ten items. However, empirically, we highly prioritize validity, significance, and novelty. Combining our experience and the statistical testing results, we developed two options: validity, significance, and feasibility; validity, significance, clinical relevance, and feasibility. The testing results indicated that both were valid options. Thus, we determined the 3-item evaluation instrument for operational purposes. We used our experience and the statistical testing results to make the decision.

Meanwhile, we noticed negative ICC values in ethicality, potential benefits and risks, and interestingness. The results indicated that reaching a consensus on these items might be challenging. We recommend that these three items change to a binary (yes/no) category instead of a 5-point Likert scale to simplify the evaluation and improve the agreement among the evaluators.

During the external validation, one major result was to add “not applicable” as an option to the evaluation instrument under each item and subitem. Considering the different backgrounds of expert panel members, this additional option helped them to simplify the evaluation process. Comparing the statistical results, we noticed a significant improvement in experimental evaluation 2, mainly due to the examples of the best and worst hypotheses, which might assist evaluators in calibrating their expectations. Furthermore, we reminded the evaluators that some statements were not hypotheses, i.e., we used validity as a screening item. The experimental evaluation 2 results are based on 17 valid hypotheses. The 13 invalid hypotheses have three or more expert panel members who evaluated them as 1 (the lowest score) in validity.

Although the evaluation of a particular hypothesis by an expert can be subjective, we used examples of the best and worst hypotheses to assist experts in calibrating their expectations more accurately. The inclusion of seven expert members balances the subjectivity and provides a more consistent evaluation using the same instrument. In addition, we used objective measures, e.g., the number of hypotheses generated and the average time spent on each hypothesis, and randomized the hypotheses during the assessment. These strategies helped the expert panel to provide more consistent evaluations and allowed us to accurately conclude the quality of the hypotheses.

The metrics and instruments developed in this study can benefit clinical researchers in evaluating their hypotheses more comprehensively, consistently, and efficiently before launching a research project, as well as providing valid instruments for the peer review process in clinical research. Our results provide an evidence-based brief version (validity, significance, and feasibility) and a comprehensive version of the evaluation items (validity, significance, feasibility, novelty, clinical relevance, testability, clarity, ethicality, potential benefits and risks, and interesting to others) to assess the quality of clinical research hypotheses. The metrics can be used to standardize the process and provide a consistent tool for this highly sophisticated cognitive process.

Supplementary Material

Supplement 1.

Appendix 1 : Initial survey instrument used for external validation of the evaluation items

Supplement 2

Appendix 2 : Medium survey instrument used for external validation of the evaluation items

Supplement 3

Appendix 3 : Evaluation instrument of 10 items with subitems (full version) to evaluate the scientific hypotheses in clinical research

Supplement 4

Appendix 4: Evaluation instrument of 3 items without subitems to evaluate the scientific hypotheses in clinical research

Acknowledgments and funding

The project was supported by the National Library of Medicine (R15LM012941) and partially supported by the National Institute of General Medical Sciences of the United States National Institutes of Health (P20GM121342).

Competing interests

None to disclose.

Agile Product and Project Management pp 69–93 Cite as

Validating the Product Hypothesis

• Mariya Breyter 2  
• First Online: 22 September 2022

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In Chapter 3 , we discussed different techniques for identifying your customer. Whether you are delivering a fixed- scope project or iteratively building a new product, your business will not be possible without having customer needs at the center of your delivery model. Customers define which businesses succeed and which businesses fail. In addition, we discussed Lean UX techniques for identifying proto-personas and understanding their needs. We also covered multiple techniques for user segmentation and analysis, including empathy maps and customer journeys.

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Angular JS Form Validation: Insights And Best Practices

Explore Angular JS form validation with practical examples in this article. We'll cover the essentials of implementing and testing validation rules, ensuring your forms are robust and user-friendly.

💡 KEY INSIGHTS

• Angular JS's built-in validation directives offer a streamlined approach for basic form validations.
• Custom validation techniques in Angular JS allow for complex validation logic , tailored to specific application needs.
• Asynchronous validation in Angular JS enables server-side checks, enhancing the robustness of form validation.
• Effective error message display strategies significantly improve user experience in form interactions.

Angular JS form validation is a crucial aspect of web development, ensuring user input is correct and useful. This article offers a clear, step-by-step example to help you implement effective form validation in your Angular JS projects. By focusing on practical implementation, you'll gain the skills needed to enhance user experience and data integrity in your applications.

Creating A Basic Form In Angular JS

Implementing validation rules, handling form submission, displaying validation feedback, testing form validation, frequently asked questions.

Creating a basic form in Angular JS is a foundational skill for any web developer working with this framework. Angular JS, known for its robustness and efficiency, offers a streamlined approach to form creation and management.

Setting Up Your Angular JS Environment

Building the form structure, integrating angular js data binding, implementing form validation, displaying validation errors.

Before diving into form creation, ensure your Angular JS environment is properly set up. This involves installing Angular JS and its dependencies. You can easily include Angular JS in your project by adding the following script tag in your HTML file:

The first step in creating a form is defining its HTML structure. In Angular JS, forms are typically created using the <form> tag, combined with various input elements. Here's a basic example:

Angular JS shines with its two-way data binding feature, which synchronizes the data between the model and the view. In the context of forms, this means that any changes to the input fields are immediately reflected in the model. The ng-model directive is used for this purpose. For instance:

Angular JS provides built-in directives for form validation. These include ng-required , ng-minlength , ng-maxlength , and ng-pattern . You can apply these directives directly to your form elements. For example:

To enhance user experience, it's important to provide feedback on validation errors. Angular JS makes this easy with its form and input states like $dirty , $touched , and $invalid . You can use these states to display error messages. For example:

Handling form submission in Angular JS involves attaching a function to the form's ng-submit directive. This function typically includes validation checks and data processing logic. Here's a simple example:

Implementing validation rules in Angular JS forms is a critical step to ensure that the data collected is accurate and useful. Angular JS provides a variety of built-in directives to facilitate this process, making it straightforward to set up complex validation logic.

Basic Validation Directives

Using 'ng-minlength' and 'ng-maxlength', implementing 'ng-pattern', custom validation with 'ng-change', handling validation feedback.

Angular JS offers several directives for basic validation, such as ng-required , ng-minlength , ng-maxlength , and ng-pattern . These can be applied directly to form elements. For instance, to make an input field required, you would use:

For controlling the length of an input, ng-minlength and ng-maxlength are incredibly useful. They ensure that inputs meet specific length requirements. Here's how you can use them:

ng-pattern is used to validate input based on a regular expression. It's particularly useful for formatting requirements, such as phone numbers or email addresses. For example, to validate an email address, you could use:

Sometimes, you might need more complex validation than what is offered by Angular's built-in directives. In such cases, ng-change can be used to implement custom validation logic. For example:

Providing feedback to the user about validation errors is crucial. Angular JS makes it easy to display error messages using form and input states like $dirty , $touched , and $invalid . For instance:

Handling form submission in Angular JS is a critical aspect of ensuring that your application interacts effectively with user input. This process involves not just capturing the data entered into the form but also validating it, processing it, and possibly preparing it for backend integration.

The 'ng-submit' Directive

Validating before submission, processing form data, resetting the form post-submission, handling server responses.

The ng-submit directive in Angular JS is used to define a function that will be called when the form is submitted. This is preferable to the traditional JavaScript submit event because it integrates seamlessly with Angular's scope and data binding. For example:

Before submitting the form, it's crucial to ensure that all data is valid. Angular JS forms have a $valid property, which can be used to check if the form meets all defined validation rules. For instance:

Once the form is submitted and validated, the next step is to process the data. This might involve transforming the data, making an API call, or clearing the form. For example:

After form submission, it's often necessary to reset the form. This can be done by resetting the model bound to the form or using Angular's $setPristine and $setUntouched methods. For example:

When submitting data to a server, handling the response is crucial. This might involve displaying a success message, handling errors, or updating the UI based on the server's response. For example:

Displaying validation feedback effectively in Angular JS forms is essential for providing a user-friendly experience. It helps users correct their inputs and understand what is expected in each form field. Angular JS offers a straightforward way to show this feedback using its built-in form controls and states.

Understanding Angular JS Form States

Displaying error messages, customizing feedback for different errors, styling validation feedback, using 'ng-messages' for simplified error handling.

Angular JS forms track the state of both the overall form and individual form controls. States like $dirty , $pristine , $valid , and $invalid are crucial for determining when to display validation messages.

To display error messages, you can use Angular's form states in combination with ng-show or ng-if . This allows you to show messages only when specific conditions are met. For instance:

Angular JS allows you to customize feedback for different types of validation errors, such as required , minlength , or pattern . You can use these error keys to display specific messages for each type of validation error. For example:

Styling the validation messages is important for making them noticeable and readable. You can use CSS classes to style these messages, possibly in different colors or fonts to distinguish them from other text. For example:

Then apply this class to your error message spans:

ng-messages is a module in Angular JS that simplifies the process of displaying error messages. It allows you to define a single block where all error messages for a particular field can be listed. For example:

Testing form validation in Angular JS is a critical step in ensuring that your application behaves as expected and provides a robust user experience. Proper testing helps catch errors early in the development process and ensures that your form validation logic works correctly under various scenarios.

Unit Testing Angular JS Forms

Integration testing, testing user interactions, automated end-to-end testing.

Unit testing in Angular JS typically involves testing individual components or functions in isolation. For form validation, this means testing each validation rule independently. Angular's dependency injection and modular nature make it well-suited for unit testing. You can use frameworks like Jasmine or Mocha for this purpose. For example:

While unit tests focus on individual components, integration tests look at how different parts of your application work together. For form validation, this could involve testing how the form interacts with other components like services or APIs. Protractor is a popular choice for integration testing in Angular JS applications.

Testing how users interact with your form is crucial. This includes testing how the form behaves when users enter data, submit the form, or trigger validation errors. Tools like Protractor can simulate user actions like clicking, typing, and submitting forms. For example:

End-to-end testing involves testing the entire application from start to finish, as a user would. This is crucial for ensuring that all parts of your application, including form validation, work together seamlessly. Tools like Protractor can automate these tests, simulating real user scenarios.

What is Angular JS form validation?

Angular JS form validation is a process in Angular JS applications where user input in forms is checked against predefined rules and conditions to ensure it meets certain standards before being processed or submitted.

Is it possible to validate forms asynchronously in Angular JS?

Yes, Angular JS supports asynchronous validation, which is useful for validations that require server-side checks, such as checking the availability of a username. This is typically done using the $asyncValidators pipeline.

What are the best practices for testing form validation in Angular JS?

Best practices include writing unit tests for individual validation rules, integration tests to check the interaction of the form with other components, and end-to-end tests to simulate user interaction with the form.

Let's check what you learned!

What Directive Is Used for Basic Required Field Validation in Angular JS?

Continue learning with these 'programming' guides.

• Exploring Angular Js Dropdown Menu: Essentials And Usage
• Angular Ngmodel And Its Usage
• The Essentials Of JavaScript Validator For Web Developers
• Angular FormGroup And Its Usage
• Angular JS Login Example: A Closer Look At Functionality And Integration

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Blog » Value Hypothesis & Growth Hypothesis: lean startup validation

Value Hypothesis & Growth Hypothesis: lean startup validation

Posted on September 16, 2021 |

You’ve come up with a fantastic idea for a startup and you need to discuss the hypothesis and its value? But you’re not sure if it’s a viable one or not. What do you do next? It’s essential to get your ideas right before you start developing them. 95% of new products fail in their first year of launch. Or to put it another way, only one in twenty product ideas succeed. In this article, we’ll be taking a look at why it’s so important to validate your startup idea before you start spending a lot of time and money developing it. And that’s where the Lean Startup Validation process gets into, alongside the growth hypothesis and value hypothesis. We’ll also be looking at the questions that you need to ask.

Table of contents

The lean startup validation methodology, the benefits of validating your startup idea, the value hypothesis, the growth hypothesis, recommendations and questions for creating and running a good hypothesis, in conclusion – take the time to validate your product.

What does it mean to validate a lean startup?

Validating your lean startup idea may sound like a complicated process, but it’s a lot simpler than you may think. It may be the case that you were already planning on carrying out some of the work.

Essentially, validating your startup when you check your idea to see if it solves a problem that your prospective customers have. You can do this by creating hypotheses and then carrying out research to see if these hypotheses are true or false. 

The best startups have always been about finding a gap in the market and offering a product or service that solves the problem. For example, take Airbnb . Before Airbnb launched, people only had the option of staying in hotels. Airbnb opened up the hospitality industry, offering cheaper accommodation to people who could not afford to stay inexpensive hotels. 

“Don’t be in a rush to get big. Be in a rush to have a great product” – Eric Ries

Validation is a crucial part of the lean startup methodology, which was devised by entrepreneur Eric Ries. The lean startup methodology is all about optimizing the amount of time that is needed to ensure a product or service is viable. 

Lean Startup Validation is a critical part of the lean startup process as it helps make sure that an idea will be successful before time is spent developing the final product.

As an example of a failed idea where more validation could have helped, take Google Glass . It sounded like a good idea on paper, but the technology failed spectacularly. Customer research would have shown that $1,500 was too much money, that people were worried about health and safety, and most importantly… there was no apparent benefit to the product.

Find out more about lean startup methodology on our blog

How to create a mobile app using lean startup methodology

The key benefit of validating your lean startup idea is to make sure that the idea you have is a viable one before you start using resources to build and promote it. 

There are other less obvious benefits too:

• It can help you fine-tune your idea. So, it may be the case that you wanted your idea to go in a particular direction, but user research shows that pivoting may be the best thing to do
• It can help you get funding. Investors may be more likely to invest in your startup idea if you have evidence that your idea is a viable one

The value hypothesis and the growth hypothesis – are two ways to validate your idea

“To grow a successful business, validate your idea with customers” – Chad Boyda

In Eric Rie’s book ‘ The Lean Startup’ , he identifies two different types of hypotheses that entrepreneurs can use to validate their startup idea – the growth hypothesis and the value hypothesis. 

Let’s look at the two different ideas, how they compare, and how you can use them to see if your startup idea could work.

The value hypothesis tests whether your product or service provides customers with enough value and most importantly, whether they are prepared to pay for this value.

For example, let’s say that you want to develop a mobile app to help dog owners find people to help walk their dogs while they are at work. Before you start spending serious time and money developing the app, you’ll want to see if it is something of interest to your target audience. 

Your value hypothesis could say, “we believe that 60% of dog owners aged between 30 and 40 would be willing to pay upwards of €10 a month for this service.”

You then find dog owners in this age range and ask them the question. You’re pleased to see that 75% say that they would be willing to pay this amount! Your hypothesis has worked! This means that you should focus your app and your advertising on this target audience. 

If the data comes back and says your prospective target audience isn’t willing to pay, then it means you have to rethink and reframe your app before running another hypothesis. For example, you may want to focus on another demographic, or look at reducing the price of the subscription.

Shoe retailer Zappos used a value hypothesis when starting out. Founder Nick Swinmurn went to local shoe stores, taking photos of the shoes and posting them on the Zappos website. Then, if customers bought the shoes, he’d buy them from the store and send them out to them. This allowed him to see if there was interest in his website, without having to spend lots of money on stock.

The growth hypothesis tests how your customers will find your product or service and shows how your potential product could grow over the years.

Let’s go back to the dog-walking app we talked about earlier. You think that 80% of app downloads will come from word-of-mouth recommendations.

You create a minimal viable product ( MVP for short ) – this is a basic version of your app that may not contain all of the features just yet. So, you then upload it to the app stores and wait for people to start downloading it. When you have a baseline of customers, you send them an email asking them how they heard of your app.

When the feedback comes back, it shows that only 30% of downloads have come from word-of-mouth recommendations. This means that your growth hypothesis has not been successful in this scenario. 

Does this mean that your idea is a bad one? Not necessarily. It just means that you may have to look at other ways of promoting your app. If you are relying on word-of-mouth recommendations to advertise it, then it could potentially fail.

Dropbox used growth hypotheses to its advantage when creating its software. The file-storage company constantly tweaked its website, running A/B tests to see which features and changes were most popular with customers, using them in the final product.

Like any good science experiment, there are things that you need to bear in mind when running your hypotheses. Here are our recommendations:

• You may be wondering which type of hypothesis you should carry out first – a growth hypothesis or a value hypothesis. Eric Ries recommends carrying out a value hypothesis first, as it makes sense to see if there is interest before seeing how many people are interested. However, the precise order may depend on the type of product or service you want to sell;
• You will probably need to run multiple hypotheses to validate your product or service. If you do this, be sure to only test one hypothesis at a time. If you end up testing multiple ones in one go, you may not be sure which hypothesis has had which result;
• Test your most critical assumption first – this is one that you are most worried about, and could affect your idea the most. It may be that solving this issue makes your product or service a viable one;
• Specific – is your hypothesis simple? If it’s jumbled or confusing, you’re not going to get the best results from it. If you’re struggling to put together a clear hypothesis, it’s probably a sign to go back to the drawing board.
• Measurable – can your hypothesis be measured? You’ll want to get tangible results so you can check if the changes you have made have worked.
• Achievable – is your hypothesis attainable? If not, you may want to break it down into smaller goals.
• Relevant – will your hypothesis prove the validity of your product or service? 
• Timely – can your hypothesis be measured in a set amount of time? You don’t want a goal that will take years to monitor and measure!
• Be as critical as possible. If you have created an idea, it is only natural that you want it to succeed. However, being objective rather than subjective will help your startup most in the long term;
• When you are carrying out customer research, use as vast a pool of people as time and money will allow. This will result in more accurate data. The great news is that you can use social media and other networking sites to reach out to potential customers and ask them their opinions;
• When carrying out customer research, be sure to ask the questions that matter. Bear in mind that liking your product or service isn’t the same as buying it. If a customer is enthusiastic about your idea, be sure to ask follow-on questions about why they like it, or if they would be willing to spend money on it. Otherwise, your data may end up being useless;
• While it is essential to have as many relevant hypotheses as possible, be careful not to have too many.  While it may sound like a good idea to try out lots of different ideas, it can actually be counter-productive. As Eric Ries said:

“Don’t bog new teams down with too much information about falsifiable hypotheses. Because if we load our teams up with too much theory, they can easily get stuck in analysis paralysis. I’ve worked with teams that have come up with hundreds of leap-of-faith assumptions. They listed so many assumptions that were so detailed and complicated that they couldn’t decide what to do next. They were paralyzed by the just sheer quantity of the list.”

“We must learn what customers really want, not what they say they want or what we think they should want.” – Eric Ries

According to CB Insights , the number one reason why startups fail is that there is no demand for the product. Many entrepreneurs have gone ahead and launched a product that they think people want, only to find that there is no market at all.

Lean Startup Validation is essential in helping your business idea to succeed. While it may seem like extra work, the additional work you do in the beginning will be of a critical advantage later down the line.

Still not 100% convinced? Take HubSpot . Before HubSpot launched its sales and marketing services, it started off as a blog. Co-founders Dharmesh Shah and Brian Halligan used this blog to validate their ideas and see what their visitors wanted. This helped them confirm that their concept was on the right lines and meant they could launch a product that people actually wanted to use.

Validating a startup idea before development is crucial because it ensures that the idea is viable and addresses a real problem that customers have. With a high failure rate of new products, validation helps avoid wasting time and resources on ideas that might not succeed.

The value hypothesis tests whether customers find enough value in a product or service to pay for it. The growth hypothesis examines how customers will discover and adopt the product over time. Both hypotheses are essential for validating the viability of a startup idea.

Eric Ries recommends starting with a value hypothesis before a growth hypothesis. Validating whether the idea provides value is crucial before considering how to promote and grow it.

When creating and running a hypothesis, consider the following: 1. Focus on testing one hypothesis at a time. 2. Test your most critical assumptions first. 3. Ensure your hypothesis follows SMART goals (Specific, Measurable, Achievable, Relevant, Timely). 4. Use a wide pool of potential customers for accurate data. 5. Ask relevant and probing questions during customer research. 6. Avoid overwhelming your team with excessive hypotheses.

Validating your product idea before development helps you avoid the top reason for startup failure—lack of demand for the product. By confirming that there is a market need and interest in your idea, you increase the chances of building a successful product.

Lean Startup Validation helps entrepreneurs avoid the mistake of launching a product that doesn’t address a genuine need. By gathering evidence and feedback early, you can make informed decisions about pivoting or refining your idea before investing significant time and resources.

Certainly. Suppose you’re developing a mobile app for dog owners to find dog-walking services. Your value hypothesis could be: “We believe that 60% of dog owners aged between 30 and 40 would be willing to pay upwards of €10 a month for this service.” You then validate this hypothesis by surveying dog owners in that age range and analyzing their responses.

The growth hypothesis examines how customers will discover and adopt your product. If, for example, you expect 80% of app downloads to come from word-of-mouth recommendations, but feedback shows only 30% are from this source, you may need to reevaluate your promotion strategy.

Yes, Lean Startup Validation can be applied to startups across various industries. Whether you’re offering a product or service, the process of testing hypotheses and gathering evidence applies universally to ensure the viability of your idea.

To gather accurate data, focus on reaching a diverse pool of potential customers through various channels, including social media and networking sites. Ask relevant questions about their preferences, willingness to pay, and potential pain points related to your idea

Being critical and objective during validation helps you avoid confirmation bias and wishful thinking. Objectivity allows you to assess whether your idea truly addresses a problem and resonates with customers, ensuring that your startup’s foundation is built on solid evidence.

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