A/B Testing

A/B testing randomly assigns users to different versions of a page, message, or feature and measures outcome differences. Statistical significance helps determine whether the effect is real or due to chance. Proper sample size, test duration, and metric selection are essential for valid decisions.

Chooses which design or message to deploy based on evidence. Prioritizes roadmap changes with measurable impact. Evaluates marketing tactics with quantified lift.

• Chooses which design or message to deploy based on evidence.
• Prioritizes roadmap changes with measurable impact.
• Evaluates marketing tactics with quantified lift.

• Define the hypothesis and success metric before testing.
• Randomization and adequate sample size are required.
• Consider long-term effects, not only short-term conversion.
• Running too many tests at once can create interference.
• Document results to build institutional learning.

An e-commerce team tests two checkout layouts. After running the test for two weeks and reaching the required sample size, the new layout shows a 5% increase in completed purchases. The team monitors returns and customer support tickets to confirm the change did not create downstream issues.

• Small sample results are enough to decide.
• Statistical significance means the change is always good.
• One test is definitive; re-testing may be needed.