mcpbeat

Ab Test Analysis

phuryn/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.

896 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
24819
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/phuryn/pm-skills --skill ab-test-analysis

The instruction itself

4 sections, as written by the author

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.

Further Reading

How to use it

Copy the folder

Take phuryn/ab-test-analysis from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.