ab-test-setup

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