Formulating Hypotheses

Formulating clear and testable hypotheses is a crucial step in designing any successful A/B test. A hypothesis provides a focused statement that you can evaluate using data from your experiment. In A/B testing, you always need two hypotheses: the null hypothesis and the alternative hypothesis.

The null hypothesis (often written as H0) is a default statement that assumes there is no effect or difference between your two groups (A and B). The alternative hypothesis (H1 or Ha) states what you expect to happen if your change has an impact.

A well-structured hypothesis is:

• Clear and specific;
• Directly testable using the data you will collect;
• Focused on a single measurable outcome.

Imagine you want to test a new "Sign Up" button color on your website. Here’s how you might structure your hypotheses:

• Null hypothesis (H0): "Changing the 'Sign Up' button color will not change the user sign-up rate."
• Alternative hypothesis (H1): "Changing the 'Sign Up' button color will increase the user sign-up rate."

Or, for a marketing campaign:

• Null hypothesis (H0): "Sending a weekly promotional email does not affect the average purchase value."
• Alternative hypothesis (H1): "Sending a weekly promotional email increases the average purchase value."

Avoid vague or untestable statements, such as "The new design is better" or "Users will like the new feature." Instead, focus on measurable results like conversion rate, average order value, or click-through rate.