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astoreyai

hypothesis-test

by astoreyai

Research Assistant Plugin for Claude Code - Complete research workflow automation with AI-detection

3🍴 3📅 Jan 8, 2026

SKILL.md


name: hypothesis-test description: "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." allowed-tools: Read, Write version: 1.0.0

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 QuestionTestAssumptions
2 groups, continuous outcomeIndependent t-testNormality, equal variance
2 measurements, same peoplePaired t-testNormality of differences
3+ groups, one factorOne-way ANOVANormality, homogeneity
3+ groups, multiple factorsFactorial ANOVANormality, homogeneity
Relationship between variablesPearson correlationLinearity, normality
Predict continuous outcomeLinear regressionLinearity, normality of residuals
2 categorical variablesChi-square testExpected frequencies ≥5
Ordinal data, 2 groupsMann-Whitney UNone (non-parametric)
Ordinal data, pairedWilcoxon signed-rankNone (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

Score

Total Score

60/100

Based on repository quality metrics

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