statistical-analyst

Name: statistical-analyst
Availability: InStock
Author: patasse97

Analyzes experiments using frequentist hypothesis tests and outputs structured results.

Updated Apr 9, 2026

Search Tags:#statistics #hypothesis-testing #effect-size #confidence-intervals #power-analysis #a-b-testing

Ready to run

npx skills add https://github.com/Patasse97/claude-skills --skill statistical-analyst

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statistical-analyst
Source: https://github.com/Patasse97/claude-skills/tree/main/engineering/statistical-analyst
Command: npx skills add https://github.com/Patasse97/claude-skills --skill statistical-analyst

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

An accessible toolkit to perform hypothesis testing, experiment design, and result interpretation so teams can make data-driven decisions with statistical rigor.

Core Features & Use Cases

Hypothesis testing for means, proportions, and categorical data with clear interpretation.
A/B test sizing, power analysis, and sample size estimation for planning experiments.
Interpretation framework (p-values, confidence intervals, and practical significance) for decision-making.

Quick Start

Run a two-sample hypothesis test on your dataset to obtain a significance verdict and a decision-ready report.

Frequently Asked Questions about statistical-analyst

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

FAQPage Schema

How do I calculate p-values and confidence intervals for A/B testing?▼

Hypothesis testing for categorical data uses the chi-square test to evaluate whether distributions of categories differ significantly, outputting p-values, confidence intervals, and effect sizes for decision-making.

What is the best way to interpret statistical and practical significance from experiment results?▼

Interpreting statistical and practical significance requires evaluating p-values alongside effect sizes and confidence intervals, providing an interpretation framework with guardrails for data-driven decision-making.

When should I use a t-test versus a z-test for my experiment data?▼

Use a z-test for proportions when analyzing rate data, and a t-test for means when comparing continuous averages, ensuring your frequentist hypothesis testing matches the correct data type for valid results.

How does chi-square hypothesis testing work for categorical data?▼