What Is a Hypothesis?

A hypothesis is a proposed explanation or assumption made based on evidence as a starting point for further investigation. In product development, it’s a guess you can test to learn more about your users and improve your product.

Why Hypothesis Testing Matters

Testing hypotheses is crucial because it helps you make informed decisions, reducing guesswork and risk. It means changes are based on evidence, leading to better products and user experiences. By testing, you validate your ideas before fully implementing them, saving time and resources.

Hypothesis-Driven Product Development

Hypothesis-driven product development is an approach where each feature or change is tested through a well-formed hypothesis. This method emphasizes evidence-based decisions, so every change has a clear purpose and measurable outcome. It helps teams focus on delivering real value to users, backed by data and insights.

Example:

Instead of implementing a feature because it "seems like a good idea," a hypothesis-driven approach would start with a specific assumption: "If we add a new payment method, we believe it will increase conversion rates because users have expressed difficulty with current payment options."

Characteristics of a Good Hypothesis

A good hypothesis should be clear, testable, and based on evidence. It should follow this format:

To achieve [desired outcome], we should implement [proposed idea], as this will [impact specific metric or opportunity]. We believe this because [evidence].

How to Phrase a Good Hypothesis

Step 1: Start with the Outcome

Clearly define what you want to achieve. This makes your hypothesis goal-oriented and focused on impact.

Example:

- Outcome: To improve booking rates.

Step 2: Describe the Proposed Change

Detail the specific change or idea you want to implement. This keeps the hypothesis precise and actionable.

Example:

- Proposed Change: Change the hero image on our homepage to one with Asian users.

Step 3: Explain the Expected Impact

State the specific metric or opportunity you expect to improve. This makes the hypothesis measurable.

Example:

- Impact: This will solve the perception that booking.com is only for Europeans.

Step 4: Provide Evidence

Back your hypothesis with evidence. This could be user research, data, or past experiences.

Example:

- Evidence: User research showed high bounce rates and lower conversion rates in Japan, coupled with feedback from Japanese users who felt the site wasn’t for them due to the images used.

Putting It All Together

Combine these elements into a single, cohesive statement.

Example:

- Full Hypothesis: To improve booking rates, we should change the hero image on our homepage to one with Asian users. This will solve the perception that booking.com is only for Europeans. We believe this because user research showed high bounce rates and lower conversion rates in Japan, coupled with feedback from Japanese users who felt the site wasn’t for them due to the images used.

Common Mistakes with Hypotheses

Bad Hypotheses:

1. Vague and Unmeasurable: "We think making the button red will be better."

2. Based on Gut Feelings: "I feel like users will prefer this new layout."

Why These Are Bad:

- They lack specific, measurable outcomes.

- They are not based on evidence or research.

Good Hypotheses:

1. Specific and Measurable: "Changing the button color to red will increase click-through rates by 10%."

2. Evidence-Based: "User testing showed that users are more likely to notice and click red buttons."

Testing Hypotheses

1. Define Your Metrics: Clearly state what you will measure to determine if the hypothesis is correct (e.g., click-through rates, conversion rates).

2. Run A/B Tests: Compare your current version (control) with the new version (variant). Randomly direct users to either version to avoid bias.

3. Set a Sample Size: Determine how many users you need to get reliable results. The larger the change you expect, the smaller the sample size can be.

4. Analyze Results: Look at the data to see if your hypothesis was correct. Did the changes you made result in the expected outcome?

Example Testing Process:

- Hypothesis: Changing the background color of the search box to pink will increase searches by 20%.

- Control (A): Current search box.

- Variant (B): Pink search box.

- Metric: Number of searches.

- Duration: Run the test for two weeks or until you reach a statistically significant number of users.

What to Do If a Hypothesis Fails

If your hypothesis is incorrect, it’s not a failure—it’s a learning opportunity. Here’s what to do:

1. Review the Data: Understand why the hypothesis didn’t hold up. Look at the metrics and user feedback.

2. Refine Your Hypothesis: Adjust your hypothesis based on what you learned. Maybe users didn’t react well to the color pink, but they might respond to another color.

3. Run New Tests: Use your revised hypothesis to conduct new experiments.

Example:

- Failed Hypothesis: "Changing the search box to pink will increase searches."

- Data Review: Bookings decreased because users found the pink search box unattractive.

- Refined Hypothesis: "Changing the search box to a more neutral color will increase searches."

Benefits of Hypothesis Testing

Focus on Outcomes:

- Hypotheses start with a clear goal, so every change aims to achieve a specific outcome.

- This focus prevents unnecessary features and keeps the team aligned on what matters.

Evidence-Based Decisions:

- Hypothesis testing relies on data, not gut feelings, leading to more reliable decisions.

- It combines both quantitative data (e.g., metrics) and qualitative insights (e.g., user feedback) for a holistic view.

Empowering Teams:

- Everyone on the team can contribute ideas, fostering a culture of innovation.

- Hypotheses provide a safe space to test ideas without fear of failure, as every test is a learning opportunity.

Iterative Improvement:

- By continuously testing and refining hypotheses, teams can iteratively improve the product.

- This approach means that the product evolves based on real user needs and behaviors.

Mitigating Risk:

- Testing before fully implementing changes reduces the risk of costly mistakes.

- It allows for small, incremental changes that can be adjusted based on test results.

Additional Tips for Hypothesis Testing

Avoid Confirmation Bias

Just like in crime investigations, avoid focusing only on evidence that supports your hypothesis. Be open to data that might contradict it. This means a balanced and accurate evaluation.

Define Clear Health Metrics

While testing, always monitor health metrics to see that improvements in one area don’t negatively impact other important aspects. For example, increased bookings should not lead to a spike in customer service calls.

Proper Randomization

Randomly direct users to either the control or variant to avoid bias. This randomization makes sure the test results are reliable and not skewed by external factors.

Set Realistic Timeframes

Determine how long to run your tests based on the expected change size and the number of visitors. Avoid stopping tests prematurely to get statistically significant results.

Document Everything

Keep detailed records of your hypotheses, tests, and results. This documentation helps track what worked and what didn’t, providing valuable insights for future experiments.