/ab-test-analysis

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

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**:

| 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 |

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.

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Further Reading

• [A/B Testing 101 + Examples](https://www.productcompass.pm/p/ab-testing-101-for-pms)
• [Testing Product Ideas: The Ultimate Validation Experiments Library](https://www.productcompass.pm/p/the-ultimate-experiments-library)
• [Are You Tracking the Right Metrics?](https://www.productcompass.pm/p/are-you-tracking-the-right-metrics)