ab-test-setup

Hypothesis lock requires defining your final hypothesis, locking the primary metric, and computing the required sample size before launching the A/B test. This hard gate prevents scope creep and ensures statistical power is validated beforehand.

Sample size calculation determines the minimum observations needed to detect a true effect with adequate statistical power. Computing the required sample size before launching an A/B test guarantees your experimental design yields valid, governance-compliant results.

The best way to set up guardrails for product experiments is by enforcing prereqs validation and execution readiness gates. Applying governance checks with explicit documentation ensures safe execution and prevents unintended negative impacts on your metrics.

Yes, A/B testing governance checks are designed for analytics-driven teams. They enforce hard gates for hypothesis lock, prereqs validation, and execution readiness while providing explicit documentation of tests and learnings for ongoing governance compliance.

Prereqs validation is required before launching to ensure execution readiness and safe test execution. It acts as a hard gate to verify that your hypothesis lock, primary metric, and sample size calculations meet experimental design and governance standards.

You need hypothesis lock for your A/B test whenever running product experiments requiring rigorous experimental design. It prevents metric manipulation by freezing the primary metric and hypothesis before data collection begins, ensuring statistical validity.