A/B Test Interpretation

Interpret the following A/B test results and derive clear recommendations.

**Test data:**
- Test period: [TIME PERIOD]
- Variant A (control): [DESCRIPTION]
- Variant B (treatment): [DESCRIPTION]
- Measured metric: [CONVERSION RATE / CTR / REVENUE / etc.]

**Results:**
- Variant A: [RESULT A] (n=[SAMPLE SIZE A])
- Variant B: [RESULT B] (n=[SAMPLE SIZE B])
- p-value: [P-VALUE] / Confidence interval: [CI]

**Please analyze:**

**1. Statistical Significance**
- Is the difference statistically significant (p < 0.05)?
- Confidence interval and practical meaning
- Statistical power of the test

**2. Practical Relevance (Effect Size)**
- How large is the actual difference in practice?
- Relative vs. absolute improvement
- Is the improvement business-relevant?

**3. Potential Confounders**
- Seasonal effects, novelty effect, sample bias
- External events during the test period
- Segmentation effects (SRM — Sample Ratio Mismatch)

**4. Business Impact Projection**
- Monthly/annual impact if rolled out
- Break-even calculation

**5. Recommendation**
- Clear decision: ship Variant B / reject / continue testing?
- Next steps and follow-up tests
- What should be improved in the next test?

Replace placeholders in [square brackets]: [TIME PERIOD][DESCRIPTION][CONVERSION RATE / CTR / REVENUE / etc.][RESULT A][SAMPLE SIZE A][RESULT B][SAMPLE SIZE B][P-VALUE]