How does it help with assumption checking?

The skill provides specific visual and statistical diagnostic methods for checking normality, independence, and homogeneity of variance, recommending solutions for any violations found.

What is the Hypothesis Test skill for Claude Code?

It is a specialized capability that guides users through selecting, running, and interpreting statistical hypothesis tests within the Claude Code environment, ensuring scientific rigor.

Does this skill support non-parametric tests?

Yes, it provides recommendations for non-parametric alternatives like Mann-Whitney U, Wilcoxon signed-rank, and Kruskal-Wallis when data violates standard parametric assumptions.

Can this skill help with reporting results for publications?

Absolutely; it includes standardized templates for reporting test statistics, degrees of freedom, p-values, and effect sizes (like Cohen's d) in a format ready for research papers.

Hypothesis Testing Guide

Name: Hypothesis Testing Guide
Author: astoreyai

byastoreyai

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Data Science & ML

Guides the selection, assumption checking, and interpretation of statistical hypothesis tests for rigorous research data analysis.

The Hypothesis Testing skill empowers researchers and data scientists using Claude Code to navigate complex statistical landscapes with precision. It provides a structured decision tree for selecting the right test based on variable types, automates assumption checking for normality and homogeneity, and offers standardized templates for reporting results including p-values and effect sizes. By bridging the gap between raw data and scientific insight, it ensures that statistical analyses are rigorous, accurate, and correctly interpreted for academic or industrial research workflows.

Key Features

01Detailed assumption checking for t-tests, ANOVA, and regression models

02Non-parametric alternatives for data violating normality or variance assumptions

03Comprehensive reference guide for common statistical tests and their requirements

04Interactive decision tree for selecting tests based on variable types and counts

05Standardized reporting templates for test statistics, p-values, and effect sizes

062 GitHub stars

Use Cases

01Validating data assumptions like normality and homogeneity before performing group comparisons

02Formatting statistical findings for academic publication or technical research reports

03Selecting the correct statistical test for experimental datasets with multiple independent variables

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add astoreyai/ai_scientist hypothesis-test

For use in Claude.ai and ChatGPT

Download Skill