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ab-test-analysis

Analyze A/B test results from CSV/Excel data with Python statistical calculations.

Updated Aug 27, 2026
Search Tags:#analysis#statistics#experiments#ab-testing#sample-size#p-value
One-click install
npx skills add https://github.com/jupitermoney/pm-superic-skills --skill ab-test-analysis-jupitermoney
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ab-test-analysis
Source: https://github.com/jupitermoney/pm-superic-skills/tree/main/pm-data-analytics/skills/ab-test-analysis
Command: npx skills add https://github.com/jupitermoney/pm-superic-skills --skill ab-test-analysis-jupitermoney

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze A/B test results with statistical rigor and translate findings into clear product decisions.

Core Features & Use Cases

  • Evaluate significance, calculate lift, p-values, and confidence intervals.
  • Validate sample size and guardrail metrics to ensure robust conclusions.
  • Use with raw data (CSV/Excel) to generate Python-based analyses when needed.

Quick Start

Analyze the provided A/B test data and output a structured results summary with significance, confidence intervals, and recommended action.

Frequently Asked Questions about ab-test-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate A/B test significance and p-values from CSV data?▼

To calculate A/B test significance from CSV data, you can use this skill to process raw CSV or Excel files, computing conversions, lifts, p-values, and confidence intervals. It leverages Python to apply statistical rigor and output a structured results summary for product decisions.

What is the best way to validate sample size and guardrail metrics for experiments?▼

Validating sample size and guardrail metrics is a core feature of this skill, ensuring your experiment conclusions are robust. It processes your raw experiment data to check these statistical boundaries, translating the findings into clear, actionable product decisions.

Does this A/B testing analysis tool require Python to process Excel files?▼

Yes, this skill requires Python to perform the underlying statistical calculations when processing your A/B test data. It accepts raw data in CSV or Excel formats and uses Python to generate accurate analysis outputs like p-values and confidence intervals.

Can I use this to analyze conversion lifts and confidence intervals for product experiments?▼

Yes, you can use this skill to evaluate conversion lifts and calculate confidence intervals for your product experiments. It analyzes your raw data to determine statistical significance and translates these metrics into a clear recommended action.

How do I translate A/B test statistical findings into clear product decisions?▼

You can translate A/B test findings into product decisions by applying this skill to your raw experiment data. It evaluates significance, calculates lift and p-values, and outputs a structured results summary that includes a recommended action for your product.

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