ab-test-analysis

Topics in the source

• A/B Test Analysis
• Context
• Instructions
• Further Reading

Evaluate A/B test results with statistical rigor and translate findings into clear product decisions. You are analyzing A/B test results for $ARGUMENTS. If the user provides data files (CSV, Excel, or analytics exports), read and analyze them directly. Generate Python scripts for statistical calculations when needed. 1. Understand the experiment: What was the hypothesis? What was changed (the variant)? What is the primary metric? Any guardrail metrics? How long did the test run? What is the traffic split? 2. Validate the test setup: Sample size: Is the sample large enough for the expected effect size? Use the formula: n = (Z²α/2 × 2 × p × (1-p)) / MDE² Flag if the test is underpowered (<80% power) Duration: Did the test run for at least 1-2 full business cycles? Randomization: Any evidence of sample ratio mismatch (SRM)? Novelty/primacy effects: Was there enough time to wash out initial behavior changes? 3. Calculate statistical significance: Conversion rate for control and variant Relative lift: (variant - control) / control × 100 p-value: Using a two-tailed z-test or chi-squared test Confidence interval: 95% CI for the difference Statistical significance: Is p < 0.05? Practical s

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