What problem does it solve? Teams often run experiments without clear hypotheses, stop tests too early, or misread results, leading to false conclusions and wasted traffic. This Skill provides a structured methodology for planning, running, and analyzing A/B tests so decisions are based on statistically valid evidence. ## Core Features & Use Cases - Hypothesis & Test Design: Builds structured hypotheses, selects test types (A/B, A/B/n, MVT, split URL), and defines primary, secondary, and guardrail metrics. - Sample Size & Duration Planning: Provides quick-reference sample size tables, duration formulas, and guidance on the peeking problem and sequential testing. - Growth Experimentation Program: Covers ICE prioritization, experiment velocity tracking, cadence rituals, and a reusable experiment playbook for compounding wins. - Use Case: A team wants to test a new pricing page headline. The Skill calculates required sample size from their baseline conversion rate and traffic, defines metrics, warns against stopping early, and produces a documented test plan. ## Quick Start Ask the assistant to help design an A/B test for a specific page or change, providing your current conversion rate and traffic volume.
