What problem does it solve? Teams often run A/B tests without clear hypotheses, adequate sample sizes, or statistical discipline, leading to false positives, inconclusive results, and wasted traffic. This Skill guides the design and analysis of experiments so results are statistically valid and actionable. ## Core Features & Use Cases - Hypothesis and Variant Design: Structures test hypotheses with a proven framework and helps isolate single variables across copy, design, CTA, and content changes. - Sample Size and Significance Guidance: Provides quick-reference sample size tables, calculator links, and rules for avoiding early peeking and premature test stops. - Experimentation Program Management: Covers ICE prioritization, experiment velocity metrics, playbook documentation, and weekly-to-quarterly review cadences. - Use Case: A growth marketer wants to test a new pricing page headline. The Skill helps define the hypothesis, calculate that 12k visitors per variant are needed to detect a 20% lift on a 10% baseline, set guardrail metrics, and document the result in an experiment playbook. ## Quick Start Help me design an A/B test for my signup page headline, including the hypothesis, required sample size, and how to know when the results are statistically significant.
