ab-test-designer
Design robust A/B test experiments. Use when testing a new feature, validating a hypothesis, or optimizing conversion rates.
What this skill does
## Domain Context This skill implements a proven product management framework. The approach combines best practices from industry leaders and is designed for practical application in day-to-day PM work. ## Input Requirements - Context about your product, feature, or problem - Relevant data, research, or constraints (recommended but optional) - Clear articulation of what you're trying to achieve # A/B Test Designer ## When to Use - Testing a new feature or design variation - Validating a hypothesis before full rollout - Optimizing conversion rates or key metrics - Choosing between multiple design approaches - Need to make a data-driven decision on a change ## What This Skill Does Helps you design rigorous A/B tests with clear hypotheses, success metrics, sample size calculations, and analysis plans. ## Instructions Help me design an A/B test for [feature/change]. Include: 1. Hypothesis - Current situation and metrics - Proposed change - Expected impact and why 2. Test Design - Primary success metric - Secondary metrics - Sample size needed - Test duration - User segments to include/exclude 3. Variants - Control (A): current experience - Variant (B): new experience - Any additional variants (C, D, etc.) 4. Risks and Controls - Potential negative impacts - Guardrail metrics - When to stop the test early 5. Analysis Plan - Statistical significance threshold - How to handle edge cases - Decision criteria Feature context: [Add context about the change you want to test] ## Best Practices - Start with a clear, falsifiable hypothesis - Choose one primary metric to avoid multiple comparison issues - Calculate sample size upfront based on expected effect size - Run tests for full weekly cycles to account for day-of-week effects - Set a minimum test duration (usually 1-2 weeks) - Define success criteria before running the test - Monitor guardrail metrics (revenue, errors, performance) ## Example **Input:** Testing new onboarding flow vs current 3-step process **Output:** Hypothesis (new 1-step flow will increase co...
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