A/B Test Verdict: Significance, Segments, and Ship/Kill

Paste your A/B test results and get a structured evaluation covering statistical significance, effect size, segment differences, and novelty risk. Outputs a clear ship, kill, or keep-testing recommendation with supporting rationale.

Analyze this A/B test:

Test description: {{what was changed}}
Control group (A): {{sample size and key metric result}}
Treatment group (B): {{sample size and key metric result}}
Primary metric: {{what you're measuring}}
Test duration: {{how long it ran}}

Analyze:
1. Is the sample size sufficient for the observed effect? (Post-hoc power analysis)
2. Is the result statistically significant? (with p-value and confidence interval)
3. What's the practical effect size? (absolute and relative difference)
4. Are there segment-level differences? (If segment data provided: {{list segments}})
5. Novelty effect risk assessment (is the uplift likely to diminish over time?)
6. Recommendation: ship it, kill it, or keep testing — with reasoning

Also flag if there are any red flags in the test design (sample ratio mismatch, peeking problem, multiple comparison issues).

Source: https://sureprompts.com/blog/ai-prompts-for-data-analysis

A/B Test Verdict: Significance, Segments, and Ship/Kill

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