ab-test-analysis skill (phuryn/pm-skills)

Contents

Install
SKILL.md (verbatim)
A/B Test Analysis
Context
Instructions
Further Reading

What it does. 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. Part of phuryn/pm-skills (PM Skills Marketplace) (phuryn/pm-skills).


Upstream	phuryn/pm-skills
Skill file	pm-data-analytics/skills/ab-test-analysis/SKILL.md
License	MIT
Author	Paweł Huryn
Fetched	2026-09-10

Install

Claude Code: claude plugin marketplace add phuryn/pm-skills then claude plugin install pm-data-analytics@pm-skills (the plugin that holds this skill).
Other agents: npx skills add phuryn/pm-skills --skill ab-test-analysis, or copy pm-data-analytics/skills/ab-test-analysis/ into ~/.claude/skills/ab-test-analysis/.
Raw file: curl -sL https://raw.githubusercontent.com/phuryn/pm-skills/HEAD/pm-data-analytics/skills/ab-test-analysis/SKILL.md

SKILL.md (verbatim)

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?
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.