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Pg Nonparametric by exekiel179 — Agent Skill

Name: Pg Nonparametric
Author: Exekiel179

Pg Nonparametric

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.

Exekiel179 013b2cf 2 files · 3.1 KB Updated Sep 17, 2026

File contents

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.

---name: pg-nonparametricdescription: 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.---# PG NonparametricUse 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.## LoadRead:- `../../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 PatternsMann-Whitney U (independent):```pythonx = 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):```pythonres = pg.wilcoxon(df["pre"], df["post"], alternative="two-sided").round(3)pg.print_table(res)```Kruskal-Wallis (k independent groups):```pythonres = pg.kruskal(data=df, dv="score", between="group").round(3)pg.print_table(res)```Friedman (k repeated conditions):```pythonres = pg.friedman(data=df, dv="score", within="condition", subject="id").round(3)pg.print_table(res)```Cochran Q (binary repeated):```pythonres = 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.

Frequently asked questions