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Pg nonparametric

Skill Exekiel179/pingouin-psych-stats/skills/pg-nonparametric

Skill plugin for Claude Code, Codex & other AI agents: small, safety-first Pingouin workflows for psychology statistics — assumption checks, S0–S5 supervision gates, reproducible Python, and APA-style reporting.

Install
npx -y skills add Exekiel179/pingouin-psych-stats --skill pg-nonparametric

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Run or generate Pingouin code for rank-based non-parametric tests — Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis, Friedman, and Cochran Q — when outcomes are ordinal or parametric assumptions fail.

SKILL.md

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PG Nonparametric

Use when the outcome is ordinal, or continuous but non-normal with small n, so a rank-based test is safer than a t test or ANOVA.

Load

Read:

  • ../../references/supervision-gates.md
  • ../../references/pingouin-api-quickref.md
  • ../../references/apa-output-template.md if writing results.

Decision Rules

  • Two independent groups -> pg.mwu(x, y) (Mann-Whitney U).
  • Same participants measured twice / paired -> pg.wilcoxon(x, y) (signed-rank).
  • Three or more independent groups, one factor -> pg.kruskal(data, dv, between).
  • Three or more repeated conditions, continuous/ordinal -> pg.friedman(data, dv, within, subject).
  • Three or more repeated conditions, binary outcome -> pg.cochran(data, dv, within, subject).
  • Follow a significant omnibus with pg.pairwise_tests(..., parametric=False, padjust="holm").

Required Inputs

  • Outcome column and its scale (ordinal or non-normal continuous).
  • Grouping (between) or condition (within) column.
  • Subject ID for paired/repeated designs.
  • Post hoc correction: default holm.
  • Alternative hypothesis: default two-sided (mwu/wilcoxon only).

Code Patterns

Mann-Whitney U (independent):

x = df.loc[df["group"].eq("A"), "score"]
y = df.loc[df["group"].eq("B"), "score"]
res = pg.mwu(x, y, alternative="two-sided").round(3)
pg.print_table(res)

Wilcoxon signed-rank (paired):

res = pg.wilcoxon(df["pre"], df["post"], alternative="two-sided").round(3)
pg.print_table(res)

Kruskal-Wallis (k independent groups):

res = pg.kruskal(data=df, dv="score", between="group").round(3)
pg.print_table(res)

Friedman (k repeated conditions):

res = pg.friedman(data=df, dv="score", within="condition", subject="id").round(3)
pg.print_table(res)

Cochran Q (binary repeated):

res = pg.cochran(data=df, dv="passed", within="condition", subject="id").round(3)
pg.print_table(res)

Output Checks

mwu/wilcoxon return U_val/W_val, p_val, RBC (rank-biserial), CLES. kruskal returns H, ddof1, p_unc; friedman returns Kendall W, Q, p_unc; cochran returns Q, dof, p_unc. Report the effect size, not just p.

Guardrails

  • mwu is unpaired only; wilcoxon is paired only — do not swap them.
  • Kruskal/Friedman/Cochran are omnibus; always follow a significant result with corrected pairwise tests.
  • Rank tests compare distributions/ranks, not means; word conclusions accordingly.
  • Prefer parametric tests when their assumptions hold (more power).
  • End result-bearing answers with one compact S0-S5 audit line.

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