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 full lifecycle of A/B testing—from hypothesis framing and sample size calculation to result analysis and playbook documentation—so experiments produce statistically valid, actionable outcomes. ## 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. - Sample Size & Duration Planning: Provides quick-reference tables, duration formulas, and guidance on sequential testing and multi-variant adjustments via detailed reference guides. - Growth Experimentation Program: Supports ICE prioritization, experiment velocity tracking, cadence rituals, and a reusable experiment playbook for compounding wins. - Use Case: A marketer wants to test a new pricing page headline. The Skill calculates required sample size from baseline conversion and traffic, defines metrics, warns against peeking early, and produces a structured test plan and results documentation template. ## Quick Start Ask the assistant to help you design an A/B test for a specific page or change, providing your current conversion rate and traffic volume.
