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hypothesis-testing-guide

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Statistical hypothesis testing, power analysis, and significance reporting

skills/analysis/statistics/hypothesis-testing-guide/SKILL.md

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# Hypothesis Testing Guide

## Overview

Hypothesis testing is the backbone of empirical research. It provides a principled framework for deciding whether observed differences in data reflect genuine effects or merely random variation. Misuse of hypothesis tests -- p-hacking, ignoring assumptions, confusing statistical and practical significance -- is a leading cause of irreproducible findings.

This guide covers the core hypothesis testing framework, the most commonly used tests across disciplines, assumption checking, effect size reporting, power analysis for sample size planning, and multiple comparison corrections. Each test is accompanied by Python code using scipy, statsmodels, and pingouin, ready to integrate into research workflows.

The goal is not just to help you run tests, but to help you run the right test correctly and report results following modern standards (APA 7th edition, journal best practices).

## The Hypothesis Testing Framework

### Step-by-Step Procedure
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