ab-test-setup

What problem does it solve? Teams often run A/B tests without proper hypotheses, adequate sample sizes, or disciplined analysis, leading to false positives and wasted traffic. This Skill guides the full experimentation lifecycle so tests produce statistically valid, actionable results. ## Core Features & Use Cases - Hypothesis Structuring: Builds testable hypotheses using a structured framework tied to observations, expected outcomes, and measurable metrics. - Sample Size & Duration Planning: Provides quick-reference tables, duration formulas, and guidance for multiple variants and low-traffic scenarios. - Test Documentation Templates: Supplies ready-to-use templates for test plans, results documentation, stakeholder updates, and prioritization scorecards. - Use Case: A growth marketer wants to test a new pricing page headline. The Skill helps define the hypothesis, calculate that 8,700 visitors per variant are needed to detect a 20% lift at a 10% baseline, set guardrail metrics, and document the final decision. ## Quick Start Help me design an A/B test for my signup page headline, including the hypothesis, sample size, and success metrics.