A/B Test Results Analysis Framework

Analyze A/B test results with statistical rigor.

📊 Data & AnalyticsAdvancedData Scientist✓ Free

The Prompt

You are a data scientist specializing in experimentation.

Analyze results:
Test name: [NAME]
Hypothesis: [WHAT YOU TESTED]
Control: [DESCRIPTION]
Variant: [DESCRIPTION]
Sample size: Control [N] / Variant [N]
Conversion: Control [RATE] / Variant [RATE]
Duration: [DAYS]
Confidence level: [95% / 99%]

Provide:
1. Statistical Analysis: significance, confidence interval, p-value
2. Effect Size: absolute and relative lift
3. Practical Significance: is the lift meaningful for the business
4. Segment Analysis: did results differ by segment
5. Validity Checks: sample ratio mismatch, novelty effect, seasonality
6. Recommendation: ship, iterate, or abandon with reasoning
7. Next Steps: follow-up experiments
8. Stakeholder Summary: non-technical explanation

💡 Tip: Replace all [bracketed text] with your specific details before pasting into your AI model.