ab-test-setup

To calculate A/B test sample size, you need your baseline conversion rate, expected lift percentage, and daily traffic volume to determine test duration and statistical power. This ensures your comparison test captures enough data to detect true effects.

A hypothesis framework for growth experimentation structures your predicted outcome by defining the problem, proposed variant change, expected metric impact, and guardrails. This prevents unfocused testing by ensuring every experiment targets a measurable conversion change.

To stop peeking at A/B test results without losing early insights, use sequential testing methods that adjust statistical thresholds for multiple interim checks. This prevents false positives while still allowing early evaluation of conversion rate data.

Use a split URL test instead of a standard A/B test when comparing drastically different page designs or backend architectures that cannot be handled by simple front-end variant swapping. This approach ensures clean comparison routing for major overhauls.

Prioritize A/B test hypotheses for marketing pages by applying ICE scoring, which evaluates potential impact, implementation ease, and confidence level. This framework ranks growth experiments to maximize conversion rate improvements and testing velocity.