Statistical Hypothesis Testing

This one gives Claude the framework to run proper statistical hypothesis tests when you're analyzing data. It covers the standard stuff: t-tests, chi-square, ANOVA, and correlation tests, plus the conceptual bits like null hypotheses, p-values, and significance levels. Useful when you need to know if your A/B test results actually mean something or you're just looking at noise. The repo has 245 stars and passed security audits from three different services, so it's been vetted. It's part of a larger collection of AI prompts, so you're getting a focused subset rather than a standalone tool. Solid if you're doing any kind of statistical analysis and want Claude to handle the mechanics correctly.

GitHub stars: 317
last updated: Aug 21, 2026

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Install

$ npx -y skills add aj-geddes/useful-ai-prompts --skill 'Statistical Hypothesis Testing' --agent claude-code

Installs into .claude/skills of the current project.

Files

Statistical Hypothesis Testing

Overview

Hypothesis testing provides a framework for making data-driven decisions by testing whether observed differences are statistically significant or due to chance.

Testing Framework

Null Hypothesis (H0): No effect or difference exists
Alternative Hypothesis (H1): Effect or difference exists
Significance Level (α): Threshold for rejecting H0 (typically 0.05)
P-value: Probability of observing data if H0 is true

Common Tests

T-test: Compare means between two groups
ANOVA: Compare means across multiple groups
Chi-square: Test independence of categorical variables
Mann-Whitney U: Non-parametric alternative to t-test
Kruskal-Wallis: Non-parametric alternative to ANOVA

Implementation with Python

import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt

# Sample data
group_a = np.random.normal(100, 15, 50)  # Mean=100, SD=15
group_b = np.random.normal(105, 15, 50)  # Mean=105, SD=15

SKILL.md on GitHub