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...