A/B Test Setup

Anwendungsfälle

• Designing experiments with proper statistical rigor
• Calculating required sample sizes for desired power
• Setting up feature flag configurations for gradual rollouts
• Defining primary and guardrail metrics for experiments
• Analyzing experiment results and making ship/no-ship decisions

Design an A/B test for the following change:

Feature: [describe the change]
Hypothesis: [what you expect to happen]
Primary metric: [what you're measuring]
Current baseline: [current metric value]
Minimum detectable effect: [smallest meaningful improvement]
Traffic: [daily active users or events]

Provide:
1. Experiment design (control vs variants)
2. Sample size calculation with 80% power, 95% confidence
3. Expected duration to reach significance
4. Guardrail metrics to monitor
5. Feature flag implementation code snippet
6. Analysis plan (when to check, how to decide)

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