Pg anova

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

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-anova

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Run or generate Pingouin code for one-way, factorial, repeated-measures, mixed, Welch ANOVA, ANCOVA, and follow-up pairwise tests.

SKILL.md

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PG ANOVA

Use when the outcome is continuous and predictors are categorical factors, optionally with covariates or repeated measures.

Load

Read:

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

Function Choice

One between-subject factor -> pg.anova(data=df, dv=..., between=..., detailed=True).
Multiple between-subject factors -> pg.anova(data=df, dv=..., between=[...], detailed=True).
Unequal variances in one-way between design -> consider pg.welch_anova.
One or more within-subject factors -> pg.rm_anova(..., within=..., subject=..., detailed=True).
One within-subject factor plus one between-subject factor -> pg.mixed_anova.
Between-subject factor plus continuous covariate -> pg.ancova.
Follow-up contrasts -> pg.pairwise_tests with padjust.

Required Inputs

Dependent variable.
Between-subject factor(s).
Within-subject factor(s).
Subject ID for repeated/mixed designs.
Covariates for ANCOVA.
Planned contrasts or post hoc intent.
Desired effect size, if not Pingouin default.

Code Patterns

One-way ANOVA:

aov = pg.anova(data=df, dv="score", between="group", detailed=True).round(3)
pg.print_table(aov)

Repeated-measures ANOVA:

aov = pg.rm_anova(data=df, dv="score", within="condition",
                  subject="id", detailed=True).round(3)
pg.print_table(aov)

Mixed ANOVA:

aov = pg.mixed_anova(data=df, dv="score", within="time",
                     between="group", subject="id").round(3)
pg.print_table(aov)

Follow-up:

posthoc = pg.pairwise_tests(data=df, dv="score", within="time",
                            between="group", subject="id",
                            padjust="holm", effsize="hedges").round(3)
pg.print_table(posthoc)

ANCOVA:

aov = pg.ancova(data=df, dv="score", between="group", covar="baseline").round(3)

Interpretation Checklist

Identify omnibus effects before pairwise claims.
For repeated-measures effects, check and report sphericity or corrected p-values if returned.
Report effect size column present in output, commonly np2.
Report correction used for post hoc comparisons.
For interactions, do not interpret main effects as simple group differences unless appropriate.

Guardrails

mixed_anova is for a specific mixed design; do not use it for arbitrary multi-level nesting.
Pingouin is not a full mixed-effects model package. For random slopes/nested clusters, recommend statsmodels/R lme4 instead.
Do not run ANOVA on wide repeated-measures data until reshaped or a Pingouin function explicitly accepts that shape.
If cell sizes are very small or missing cells exist, inspect design balance before trusting output.
End result-bearing answers with one compact S0-S5 audit line.