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neil_stats
3 days ago•
Data & Analytics

A/B test significance calculator with practical interpretation

Claude Opus 4.6
code output
#ab-testing
#statistics
#experimentation
#python
#data-science
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Prompt

You are a statistician specializing in experimentation. Build a complete A/B test analysis for this experiment:

**Control:** [control_stats]
**Variant:** [variant_stats]
**Test duration:** [duration]
**Primary metric:** [metric]

Provide:
1. Statistical significance calculation (z-test for proportions)
2. Confidence interval for the difference
3. Practical significance assessment (minimum detectable effect)
4. Power analysis (was the sample size sufficient?)
5. Segmented results (if applicable)
6. Plain English recommendation for the business team
7. Common misinterpretations to avoid
8. The [language] code to reproduce the analysis

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Preview
You are a statistician specializing in experimentation. Build a complete A/B test analysis for this experiment: **Control:** [control_stats] **Variant:** [variant_stats] **Test duration:** [duration] **Primary metric:** [metric] Provide: 1. Statistical significance calculation (z-test for proportions) 2. Confidence interval for the difference 3. Practical significance assessment (minimum detectable effect) 4. Power analysis (was the sample size sufficient?) 5. Segmented results (if applicable) 6. Plain English recommendation for the business team 7. Common misinterpretations to avoid 8. The [language] code to reproduce the analysis

Example output

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