skills.rest
Patasse97
Patasse97COMMUNITY
Explore all skills by this author →
GitHub

statistical-analyst

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
One-click install
npx skills add https://github.com/Patasse97/claude-skills --skill statistical-analyst
Or copy as Structured Prompt for Agent▼
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?▼

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.

Related Skills

Explore More Data & Analytics Skills→
Thiago-Cruz-engThiago-Cruz-eng

data-analysis

Analyze datasets to extract insights and produce structured reports.

Community
Intermediate
nimrodfishernimrodfisher
351

ab-test-analysis

Compute statistical significance, confidence intervals, and effect sizes for A/B test variants.

Community
Advanced
kongaharshakongaharsha
1

statistical-analysis

Summarize data, detect anomalies, and assess significance with statistical methods.

Community
Advanced
diegoscarabellidiegoscarabelli
9

statistical-analysis

Analyze quantitative data with hypothesis testing and Bayesian modeling.

Community
Advanced