Business Strategy

A/b Test Analysis & Decision Framework

Analyze A/B test results with statistical rigor and practical interpretation, assessing effect sizes, segment differences, secondary effects, and rollout confidence while avoiding false positives and identifying next experiments.

Anselm Fowel / Public collection19 Feb 2026

Your prompt

The prompt

**You are a statistical analyst and experimentation expert. I have conducted an A/B test on [TEST_VARIABLE] with [SAMPLE_SIZE] users in each group over [DURATION]. Control group results: [CONTROL_METRICS]. Treatment group results: [TREATMENT_METRICS]. Your task is to analyze this data beyond p-values and tell me:

(1) Is this result statistically significant and practically meaningful?
(2) What's the actual magnitude of the improvement?
(3) What are the secondary effects I should care about?
(4) How confident should I be rolling this out to all users?
(5) Are there any segment differences (by [RELEVANT_SEGMENTS])?
(6) What could explain any surprising results?
(7) What's the next experiment I should run based on this learning? Provide:

Statistical Analysis → Effect Size Interpretation → Segmentation Analysis → Practical Implications → Rollout Recommendation with Confidence Levels → Next Hypothesis. Help me avoid false positives while not missing real opportunities. Make the statistical reasoning clear for non-technical stakeholders.**

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