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ab-test-analysis
GitHub用于分析A/B测试结果，验证样本量与统计显著性，检查护栏指标，并基于数据给出上线、延长或停止实验的决策建议。
Trigger Scenarios
评估实验结果
检查测试是否达到显著性
解释分裂测试数据
决定是否发布变体
Install
npx skills add phuryn/pm-skills --skill ab-test-analysis -g -y
SKILL.md
Frontmatter
{
"name": "ab-test-analysis",
"description": "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
-
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?
-
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?
- Sample size: Is the sample large enough for the expected effect size?
-
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.
-
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
-
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 -
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
Version History
- 18468a9 Current 2026-07-25 10:30
