Ab Testing

This helps you design and run A/B tests that actually produce valid results instead of noise. It walks you through hypothesis formation, sample size calculations, variant design, and statistical analysis with specific frameworks like "Because X, we believe Y will cause Z" and ICE scoring for prioritization. Goes beyond single tests to cover building a continuous experimentation program with backlog management and playbook development. The emphasis on not peeking early and pre-committing to sample sizes is refreshingly direct about the discipline required. Includes sample size tables, metric selection guidance, and documentation templates. Works with product marketing context files if they exist to avoid asking redundant questions.