A/B Testing Analysis Framework

Create an A/B testing analysis framework.

**Test Type:** ${{TEST_TYPE}}
**Test Duration:** ${{DURATION}}
**Sample Size:** ${{SAMPLE_SIZE}}
**Primary Metric:** ${{METRIC}}
**Confidence Level:** ${{CONFIDENCE}}

Generate the framework:

**Test Setup Documentation:**
- Test hypothesis
- Control description
- Variation description
- Success criteria
- Audience allocation

**Statistical Analysis:**

**Sample Size:**
- Control sample
- Variation sample
- Total sample
- Sample adequacy

**Primary Metric:**
- Control performance
- Variation performance
- Absolute difference
- Relative lift
- Confidence interval

**Statistical Significance:**
- P-value
- Confidence level
- Statistical power
- Minimum detectable effect

**Secondary Metrics:**
| Metric | Control | Variation | Lift | Significant? |
|--------|---------|-----------|------|--------------|

**Segment Analysis:**
- Performance by segment
- Segment-level significance
- Interaction effects
- Unexpected patterns

**Results Interpretation:**

**Winner Determination:**
- Clear winner criteria
- Practical significance
- Statistical significance
- Business impact

**Result Categories:**
- Clear winner
- Inconclusive
- No difference
- Negative result

**Confidence Assessment:**
- Data quality
- External factors
- Validity threats
- Replication needs

**Business Impact:**
- Revenue impact
- Conversion impact
- Cost impact
- Long-term effects

**Recommendations:**
- Implementation decision
- Rollout plan
- Monitoring needs
- Follow-up tests

**Documentation:**

**Test Report:**
- Hypothesis
- Methodology
- Results
- Insights
- Recommendations

**Learning Database:**
- Test summary
- Key findings
- Applicable insights
- Future implications

**Process Improvement:**
- Methodology refinement
- Tool improvements
- Team learnings