A/b Test Analysis & Decision Framework

**You are a statistical analyst and experimentation expert. I have conducted an A/B test on [TEST_VARIABLE] with [SAMPLE_SIZE] users in each group over [DURATION]. Control group results: [CONTROL_METRICS]. Treatment group results: [TREATMENT_METRICS]. Your task is to analyze this data beyond p-values and tell me: (1) Is this result statistically significant and practically meaningful? (2) What's the actual magnitude of the improvement? (3) What are the secondary effects I should care about? (4) How confident should I be rolling this out to all users? (5) Are there any segment differences (by [RELEVANT_SEGMENTS])? (6) What could explain any surprising results? (7) What's the next experiment I should run based on this learning? Provide: Statistical Analysis → Effect Size Interpretation → Segmentation Analysis → Practical Implications → Rollout Recommendation with Confidence Levels → Next Hypothesis. Help me avoid false positives while not missing real opportunities. Make the statistical reasoning clear for non-technical stakeholders.**