| 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/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.
