ab-test-analysis

Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.

A/B Test Analysis

Evaluate A/B test results with statistical rigor and translate findings into clear product decisions.

Context

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.

Instructions

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?

1. 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?

1. 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 significance: Is the lift meaningful for the business?

If the user provides raw data, generate and run a Python script to calculate these.

1. Check guardrail metrics:

• Did any guardrail metrics (revenue, engagement, page load time) degrade?
• A winning primary metric with degraded guardrails may not be a true win

1. Interpret results:

| Outcome | Recommendation | |---|---| | Significant positive lift, no guardrail issues | Ship it — roll out to 100% | | Significant positive lift, guardrail concerns | Investigate — understand trade-offs before shipping | | Not significant, positive trend | Extend the test — need more data or larger effect | | Not significant, flat | Stop the test — no meaningful difference detected | | Significant negative lift | Don't ship — revert to control, analyze why |

1. Provide the analysis summary:

``` ## A/B Test Results: [Test Name]

Hypothesis: [What we expected] Duration: [X days] | Sample: [N control / M variant]

| Metric | Control | Variant | Lift | p-value | Significant? | |---|---|---|---|---|---| | [Primary] | X% | Y% | +Z% | 0.0X | Yes/No | | [Guardrail] | ... | ... | ... | ... | ... |

Recommendation: [Ship / Extend / Stop / Investigate] Reasoning: [Why] Next steps: [What to do] ```

Think step by step. Save as markdown. Generate Python scripts for calculations if raw data is provided.

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Further Reading

• A/B Testing 101 + Examples
• Testing Product Ideas: The Ultimate Validation Experiments Library
• Are You Tracking the Right Metrics?