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 unreliable decisions. This Skill provides a structured methodology for planning, running, and analyzing experiments that produce statistically valid, actionable results. ## Core Features & Use Cases - Hypothesis and Test Design: Structures hypotheses with a proven framework, defines primary/secondary/guardrail metrics, and guides variant design and traffic allocation. - Sample Size and Duration Planning: Provides quick-reference sample size tables by baseline conversion rate and minimum detectable effect, plus duration calculation formulas and sequential testing guidance. - Growth Experimentation Program: Covers ICE prioritization, experiment velocity tracking, playbook documentation, and cadence rituals for running experiments as a continuous growth engine. - Use Case: A product marketer wants to test a new pricing page headline. The Skill helps calculate that they need 8,700 visitors per variant to detect a 20% lift on a 10% baseline, provides a test plan template, and defines guardrail metrics like refund rate before launch. ## Quick Start Ask the assistant to help design an A/B test for your pricing page headline, including the hypothesis, required sample size, and success metrics.
