pg-anova

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

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/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.