A/B Test Statistical Analysis and Recommendation Engine
Analytics & ReportingadvancedClaude 3.5 Sonnet or GPT-4o. Claude excels at nuanced statistical reasoning and clearly explaining confidence intervals to non-technical stakeholders. GPT-4o offers faster processing for large datasets and slightly better structured output formatting. Both handle the mathematical rigor required for statistical analysis.
When to Use This Prompt
Use this prompt when you need rigorous statistical validation of A/B test results before making implementation decisions. It's essential for high-stakes tests (pricing, checkout flows, core features) where false positives could waste resources or harm user experience. Perfect for CMOs who need to justify decisions to leadership with statistical confidence.
The Prompt
You are a statistical analyst specializing in marketing A/B testing. Analyze the following A/B test results and provide actionable insights with statistical rigor.
## Test Parameters
- Test Name: [TEST_NAME]
- Duration: [START_DATE] to [END_DATE]
- Sample Size: Control [CONTROL_N], Variant [VARIANT_N]
- Primary Metric: [METRIC_NAME]
- Baseline (Control): [CONTROL_VALUE] [UNIT]
- Variant Result: [VARIANT_VALUE] [UNIT]
- Confidence Level Target: [CONFIDENCE_LEVEL]% (typically 95%)
- Statistical Test Type: [TEST_TYPE] (e.g., two-sample t-test, chi-square, z-test)
## Secondary Metrics (if applicable)
- Metric 1: Control [CONTROL_VALUE_1] vs Variant [VARIANT_VALUE_1]
- Metric 2: Control [CONTROL_VALUE_2] vs Variant [VARIANT_VALUE_2]
- Metric 3: Control [CONTROL_VALUE_3] vs Variant [VARIANT_VALUE_3]
## Additional Context
- Industry/Product Type: [INDUSTRY]
- Business Impact of Improvement: [BUSINESS_CONTEXT]
- Cost of Implementation: [COST_IF_KNOWN]
- Audience Segment: [SEGMENT_DESCRIPTION]
## Analysis Requirements
1. Calculate statistical significance (p-value, confidence interval, effect size)
2. Determine if results are statistically significant at the target confidence level
3. Assess practical significance—does the improvement matter for business?
4. Identify any potential issues (sample size adequacy, test duration, external factors)
5. Evaluate secondary metrics for trade-offs or unexpected effects
6. Provide a clear recommendation: implement, reject, or extend testing
7. Estimate business impact if implemented (revenue, conversion lift, cost savings)
8. Suggest next steps and follow-up tests
## Output Format
Provide analysis in this structure:
- Executive Summary (1-2 sentences)
- Statistical Findings (p-value, confidence interval, effect size)
- Practical Significance Assessment
- Risk Factors and Caveats
- Business Impact Projection
- Final Recommendation
- Next Steps
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Tips for Best Results
- 1.Always include sample sizes and test duration—undersized tests or short durations are red flags that invalidate results regardless of p-values.
- 2.Request effect size calculations, not just p-values. A statistically significant result with trivial effect size may not justify implementation costs.
- 3.Ask the AI to flag secondary metrics and potential trade-offs. Winning on conversion but losing on AOV or retention is a hidden failure.
- 4.Have the AI estimate business impact in revenue or cost terms. This translates statistical significance into language that resonates with executives and boards.
Example Output
## Executive Summary
The variant shows a statistically significant 12.3% improvement in conversion rate with 96.2% confidence, translating to approximately $47,000 in additional monthly revenue.
## Statistical Findings
- Control Conversion Rate: 3.24% (n=15,420)
- Variant Conversion Rate: 3.64% (n=15,380)
- Absolute Lift: 0.40 percentage points
- Relative Lift: 12.3%
- P-value: 0.0187 (significant at 95% confidence)
- 95% Confidence Interval: [0.08%, 0.72%]
- Effect Size (Cohen's h): 0.067 (small but meaningful)
## Practical Significance Assessment
While the effect size is small by statistical standards, the improvement is practically significant given the high volume of transactions. A 12.3% lift on 50,000 monthly conversions equals 6,150 additional conversions, justifying implementation costs.
## Risk Factors and Caveats
- Test ran for 18 days; recommend monitoring for 2 weeks post-launch for consistency
- Secondary metric (average order value) showed no significant change, reducing risk of cannibalization
- Mobile traffic represented 58% of sample; consider segment-specific analysis
## Business Impact Projection
Assuming $7.60 average order value and 35% contribution margin: $47,000 monthly incremental revenue, $16,450 monthly contribution profit.
## Final Recommendation
Implement immediately. Statistical and practical significance both support rollout.
## Next Steps
1. Monitor post-launch performance for 2 weeks
2. Segment analysis by device type and traffic source
3. Test interaction effects with concurrent campaigns
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Trusted by 10,000+ Directors and CMOs.