What problem does it solve? Teams often run A/B tests without clear hypotheses, adequate sample sizes, or statistical rigor, leading to false positives, wasted traffic, and wrong product decisions. This Skill provides a structured methodology for designing, running, and analyzing experiments that produce trustworthy results. ## Core Features & Use Cases - Hypothesis & Test Design: Structures hypotheses with a proven framework, selects test types (A/B, A/B/n, MVT, split URL), and defines primary, secondary, and guardrail metrics. - Statistical Rigor: Provides sample size reference tables, duration guidance, significance thresholds, and analysis checklists to avoid the peeking problem and premature test stops. - Experimentation Program Building: Covers ICE prioritization, experiment velocity tracking, playbook documentation, and weekly-to-quarterly cadences for running growth experiments as a continuous engine. - Use Case: A product marketer wants to test a new pricing page headline. The Skill helps calculate the required sample size from the baseline conversion rate, define guardrail metrics like refund rate, and document the result in a reusable experiment playbook. ## Quick Start Ask the assistant to design an A/B test for your pricing page headline change, including hypothesis, sample size, and success metrics.
