An A/B test compares two or more experiences by assigning eligible users to different variations and measuring a predefined outcome. It can help teams estimate whether a change caused an improvement.
Begin with a hypothesis
State the customer problem, proposed change and expected effect. Testing random variations without a reason creates results that are difficult to interpret.
Choose one primary outcome
Define the decision metric and important guardrails before launch. Avoid changing success criteria after seeing the data.
Protect the comparison
Assign users consistently, run variations at the same time and confirm that tracking works equally in every group. Determine sample and duration requirements before stopping.
Interpret uncertainty
A result can be affected by chance, seasonality, audience changes or implementation errors. Statistical significance does not automatically imply a meaningful business effect.
Learn after the test
Document the hypothesis, method, result and limitations. Replicate important findings when risk is high.
Experiments support judgment; they do not replace it. Accessibility, ethics and customer trust remain requirements even when a variation converts better.
