What problem does it solve? Teams often run experiments without clear hypotheses, adequate sample sizes, or disciplined analysis, leading to false positives and wasted traffic. This Skill guides the planning, execution, and analysis of A/B tests so results are statistically valid and actionable. ## Core Features & Use Cases - Hypothesis-Driven Test Design: Structures experiments using a formal hypothesis framework, defines primary, secondary, and guardrail metrics, and selects the right test type (A/B, A/B/n, MVT, split URL). - Sample Size & Duration Planning: Provides quick-reference tables, duration formulas, and guidance on the peeking problem, sequential testing, and multi-variant adjustments. - Growth Experimentation Program: Supports building an ongoing experimentation engine with ICE prioritization, experiment velocity tracking, and a reusable playbook of winning patterns. - Use Case: A marketer wants to test a new pricing page headline. The Skill calculates the required sample size from the 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.
