How do I install the Hypothesis Test skill?

Run npx skillmds@latest add majiayu000/hypothesis-test in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Which AI agents work with Hypothesis Test?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Hypothesis Test free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Hypothesis Test?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Hypothesis Test by majiayu000 — Agent Skill

Name: Hypothesis Test
Rating: 4.2 (5 reviews)
Author: majiayu000

Hypothesis Test

Guide selection and interpretation of statistical hypothesis tests. Use when: (1) Choosing appropriate test for research data, (2) Checking assumptions before analysis, (3) Interpreting test results correctly, (4) Reporting statistical findings, (5) Troubleshooting assumption violations.

majiayu000 Updated Sep 9, 2026 567 repo stars

File contents

Hypothesis Testing Skill

Purpose

Guide appropriate selection and interpretation of statistical hypothesis tests for research data analysis.

Test Selection Decision Tree

Step 1: How many variables?

One variable:

Categorical → Chi-square goodness of fit
Continuous → One-sample t-test

Two variables:

Both categorical → Chi-square test of independence
One categorical, one continuous → T-test or ANOVA
Both continuous → Correlation or regression

Three+ variables:

Multiple predictors → Multiple regression or ANOVA
Complex designs → Mixed models or advanced methods

Step 2: Check assumptions

For t-tests:

Independence of observations
Normality (especially for small N)
Homogeneity of variance

Violations?

Non-normal → Mann-Whitney U (non-parametric)
Unequal variance → Welch's t-test
Dependent observations → Paired t-test or mixed models

For ANOVA:

Independence
Normality
Homogeneity of variance
No outliers

Violations?

Non-normal → Kruskal-Wallis test
Unequal variance → Welch's ANOVA
Outliers → Robust methods or transformation

Step 3: Interpret results

Always report:

Test statistic (t, F, χ²)
Degrees of freedom
p-value
Effect size with CI
Descriptive statistics

Example:

Independent samples t-test showed a significant difference between 
groups, t(98) = 3.45, p < .001, d = 0.69, 95% CI [0.29, 1.09]. 
The experimental group (M = 45.2, SD = 8.3) scored higher than 
control (M = 37.8, SD = 9.1).

Common Tests Reference

Research Question	Test	Assumptions
2 groups, continuous outcome	Independent t-test	Normality, equal variance
2 measurements, same people	Paired t-test	Normality of differences
3+ groups, one factor	One-way ANOVA	Normality, homogeneity
3+ groups, multiple factors	Factorial ANOVA	Normality, homogeneity
Relationship between variables	Pearson correlation	Linearity, normality
Predict continuous outcome	Linear regression	Linearity, normality of residuals
2 categorical variables	Chi-square test	Expected frequencies ≥5
Ordinal data, 2 groups	Mann-Whitney U	None (non-parametric)
Ordinal data, paired	Wilcoxon signed-rank	None (non-parametric)

Assumption Checking

Normality

Visual: Q-Q plot, histogram
Statistical: Shapiro-Wilk test (N < 50), Kolmogorov-Smirnov (N ≥ 50)
Guideline: Robust to moderate violations if N ≥ 30

Homogeneity of Variance

Visual: Box plots, residual plots
Statistical: Levene's test, Bartlett's test
Guideline: Ratio of largest/smallest variance < 4

Independence

Check: Research design, data collection
Red flags: Time series, clustered data, repeated measures
Solution: Use appropriate model (mixed effects, GEE)

Integration

Use with data-analyst agent for complete statistical analysis workflow and experiment-designer agent for planning appropriate analyses.

Version: 1.0.0

---name: hypothesis-testdescription: Guide selection and interpretation of statistical hypothesis tests. Use when: (1) Choosing appropriate test for research data, (2) Checking assumptions before analysis, (3) Interpreting test results correctly, (4) Reporting statistical findings, (5) Troubleshooting assumption violations.--- # Hypothesis Testing Skill ## Purpose Guide appropriate selection and interpretation of statistical hypothesis tests for research data analysis. ## Test Selection Decision Tree ### Step 1: How many variables? **One variable:** - Categorical → Chi-square goodness of fit - Continuous → One-sample t-test **Two variables:** - Both categorical → Chi-square test of independence - One categorical, one continuous → T-test or ANOVA - Both continuous → Correlation or regression **Three+ variables:** - Multiple predictors → Multiple regression or ANOVA - Complex designs → Mixed models or advanced methods ### Step 2: Check assumptions **For t-tests:** 1. Independence of observations 2. Normality (especially for small N) 3. Homogeneity of variance **Violations?** - Non-normal → Mann-Whitney U (non-parametric) - Unequal variance → Welch's t-test - Dependent observations → Paired t-test or mixed models **For ANOVA:** 1. Independence 2. Normality 3. Homogeneity of variance 4. No outliers **Violations?** - Non-normal → Kruskal-Wallis test - Unequal variance → Welch's ANOVA - Outliers → Robust methods or transformation ### Step 3: Interpret results Always report: 1. **Test statistic** (t, F, χ²) 2. **Degrees of freedom** 3. **p-value** 4. **Effect size with CI** 5. **Descriptive statistics** **Example:** ``` Independent samples t-test showed a significant difference between groups, t(98) = 3.45, p < .001, d = 0.69, 95% CI [0.29, 1.09]. The experimental group (M = 45.2, SD = 8.3) scored higher than control (M = 37.8, SD = 9.1). ``` ## Common Tests Reference | Research Question | Test | Assumptions | |------------------|------|-------------| | 2 groups, continuous outcome | Independent t-test | Normality, equal variance | | 2 measurements, same people | Paired t-test | Normality of differences | | 3+ groups, one factor | One-way ANOVA | Normality, homogeneity | | 3+ groups, multiple factors | Factorial ANOVA | Normality, homogeneity | | Relationship between variables | Pearson correlation | Linearity, normality | | Predict continuous outcome | Linear regression | Linearity, normality of residuals | | 2 categorical variables | Chi-square test | Expected frequencies ≥5 | | Ordinal data, 2 groups | Mann-Whitney U | None (non-parametric) | | Ordinal data, paired | Wilcoxon signed-rank | None (non-parametric) | ## Assumption Checking ### Normality ``` Visual: Q-Q plot, histogram Statistical: Shapiro-Wilk test (N < 50), Kolmogorov-Smirnov (N ≥ 50) Guideline: Robust to moderate violations if N ≥ 30 ``` ### Homogeneity of Variance ``` Visual: Box plots, residual plots Statistical: Levene's test, Bartlett's test Guideline: Ratio of largest/smallest variance < 4 ``` ### Independence ``` Check: Research design, data collection Red flags: Time series, clustered data, repeated measures Solution: Use appropriate model (mixed effects, GEE) ``` ## Integration Use with data-analyst agent for complete statistical analysis workflow and experiment-designer agent for planning appropriate analyses. --- **Version:** 1.0.0

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