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 provides a structured framework for planning, running, and analyzing A/B tests and building a continuous experimentation program. ## Core Features & Use Cases - Hypothesis & Test Design: Builds structured hypotheses, 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 sample size tables, duration formulas, and guidance on the peeking problem and sequential testing. - Experimentation Program Management: Covers ICE prioritization, experiment velocity tracking, playbooks, and documentation templates for a continuous growth loop. - Use Case: A product marketer wants to test a new pricing page headline. The Skill calculates the required sample size from the baseline conversion rate, defines metrics, warns against stopping early, and produces a documented test plan. ## Quick Start Ask the assistant to help you design an A/B test for a specific page, providing your current conversion rate, monthly traffic, and the change you want to test.
