nonparametric-tests-guide
Apply Mann-Whitney, Kruskal-Wallis, and other nonparametric methods
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill nonparametric-tests-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Nonparametric Tests Guide A skill for selecting and applying nonparametric statistical tests when data violate parametric assumptions. Covers rank-based tests for group comparisons, correlation, and paired data, with implementation examples and guidance on reporting. ## When to Use Nonparametric Tests ### Decision Criteria ``` Use nonparametric tests when: - Data are ordinal (Likert scales, rankings) - Distribution is clearly non-normal (heavy skew, outliers) - Sample size is very small (n < 15-20 per group) - Homogeneity of variance is violated - You are analyzing ranks or medians rather than means Use parametric tests when: - Data are approximately normal (or n > 30 by CLT) - Variance is homogeneous across groups - You need greater statistical power - The parametric assumptions are reasonably met ``` ### Test Selection Guide | Parametric Test | Nonparametric Alternative | Use Case | |----------------|--------------------------|----------| | Independent t-test | Mann-Whitney U | Compare 2 independent groups | | Paired t-test | Wilcoxon signed-rank | Compare 2 related samples | | One-way ANOVA | Kruskal-Wallis H | Compare 3+ independent groups | | Repeated measures ANOVA | Friedm
- When to Use Nonparametric Tests
- Decision Criteria
- Test Selection Guide
- Mann-Whitney U Test
- Two Independent Groups
- Kruskal-Wallis H Test
- Three or More Independent Groups
- Wilcoxon Signed-Rank Test
- Paired or Repeated Measures
- Spearman Rank Correlation
- Monotonic Association
- Reporting Nonparametric Results
- APA-Style Reporting Examples
- Effect Size Guidelines
What does the nonparametric-tests-guide skill do?
Apply Mann-Whitney, Kruskal-Wallis, and other nonparametric methods
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill nonparametric-tests-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.