pg-anovalisted

# 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: ```python aov = pg.anova(data=df, dv="score", between="group", detailed=True).round(3) pg.print_table(aov) ``` Repeated-measures ANOVA: ```python aov = pg.rm_anova(data=df, dv="score", within="condition", subject="id", detailed=True).round(3) pg.pr