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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.

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ab-test-analysis

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Use ab-test-analysis to analyze [experiment hypothesis, variant, results, duration, traffic split, and guardrail metrics] and deliver a statistical A/B-test report with setup validation, confidence intervals, practical-significance assessment, and a ship, extend, stop, or investigate recommendation.

Required inputs

  • [experiment hypothesis, variant, results, duration, traffic split, and guardrail metrics]

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SKILL.md

name: ab-test-analysisdescription: "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?

  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 significance: Is the lift meaningful for the business?

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

  4. 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

  5. Interpret results:

    OutcomeRecommendation
    Significant positive lift, no guardrail issuesShip it — roll out to 100%
    Significant positive lift, guardrail concernsInvestigate — understand trade-offs before shipping
    Not significant, positive trendExtend the test — need more data or larger effect
    Not significant, flatStop the test — no meaningful difference detected
    Significant negative liftDon't ship — revert to control, analyze why

  6. 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.

Further Reading