ab-test-setup

Calculate A/B test sample size by inputting your baseline conversion rate and the minimum detectable lift. The generated plan provides the required traffic allocation and estimated test duration to reach statistical significance.

Define an A/B test hypothesis by describing the feature variation and the expected impact on a primary metric. The strategy plan documents this hypothesis to ensure your experimental design targets a measurable outcome.

Guardrails and secondary metrics protect against unintended business drops during A/B testing. The plan defines these alongside primary metrics to ensure statistical significance assessment captures both target wins and risk mitigation.

Pre-launch checks for an A/B test include validating traffic allocation, confirming sample size requirements, and reviewing guardrail metrics. The generated execution steps and checklists ensure your experimental design is ready for valid analysis.

Determine an A/B test winner by applying pre-defined decision rules and analyzing statistical significance. The plan outlines analysis criteria to evaluate the primary metric, ensuring you capture accurate learnings from the experiment.

Yes, A/B test plans can include segment considerations for targeted experimental design. Defining traffic allocation and sample size for specific user segments ensures your statistical significance calculations match the intended audience.