ab-test-setup

A/B test design requires translating product goals into a testable hypothesis and defining clear success metrics. You must calculate sample size and test duration to ensure statistical rigor and avoid premature conclusions or early stopping bias.

For conversion rate optimization, track primary metrics to measure success, secondary metrics for context, and guardrail metrics to prevent negative impacts. Ensure tracking verification and QA are completed before launching the experiment.

Calculate test duration and traffic allocation by determining the necessary sample size based on your baseline conversion rate and the minimum detectable lift. This ensures your A/B test runs long enough to reach statistical significance without peeking bias.

Yes, this A/B test planning approach applies to A/B, A/B/n, multivariate (MVT), and split-URL experiment planning. It handles web and product changes including copy, layout, CTA, and pricing variations across different experiment types.

Peeking at A/B test results before completion introduces early stopping bias, leading to unreliable winners. Establishing anti-peeking decision rules and waiting for the calculated sample size ensures your conversion rate optimization results are statistically valid.

A pre-launch checklist for CRO experimentation verifies metric tracking alignment, confirms traffic allocation, and validates QA. It ensures primary, secondary, and guardrail metrics are correctly measured before your A/B test goes live.