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Analysis

Analyze A/B Test Results Rigorously

A statistics-first review of an A/B test that goes past a single p-value before you ship the result. - Inputs: control and treatment group results, primary metric, test duration, and optional segments - Checks: post-hoc power, statistical significance with confidence interval, absolute and relative effect size - Flags: novelty-effect risk, sample ratio mismatch, peeking problems, and multiple-comparison issues - Output: a ship/kill/keep-testing call with the reasoning behind it

Prompt
Analyze this A/B test:

Test description: {test_description}
Control group (A): {control_results}
Treatment group (B): {treatment_results}
Primary metric: {primary_metric}
Test duration: {test_duration}

Assess:
1. Whether the sample size is adequate given the observed effect (post-hoc power analysis)
2. Whether the result is statistically significant (p-value and confidence interval)
3. The practical size of the effect (absolute and relative difference)
4. Whether segment-level differences exist (if provided: {segments})
5. Novelty-effect risk (could the uplift fade with time?)
6. A recommendation — ship it, kill it, or keep testing — with your reasoning

Also call out any red flags in the test design itself (sample ratio mismatch, peeking problem, multiple-comparison issues).
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