Summary
- Expert guidance for designing statistically valid A/B tests and experiments.
- Provides a structured hypothesis framework, sample size calculations, and metrics selection (primary, secondary, guardrail) to ensure rigorous test design Covers test types (A/B, A/B/n, MVT, split URL), traffic allocation strategies, and implementation approaches (client-side vs. server-side) Includes pre-launch checklists, guidance on avoiding common pitfalls like early peeking, and frameworks for analyzing results with statistical significance Helps determine baseline requirements, minimum detectable effect, and test duration based on traffic volume and conversion rates