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Run or generate Pingouin code for one-way, factorial, repeated-measures, mixed, Welch ANOVA, ANCOVA, and follow-up pairwise tests.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Screen psychology datasets before Pingouin analysis: variable types, missingness, duplicates, long/wide shape, assumption checks, and safer preprocessing.
Run or generate Pingouin code for multivariate comparisons — Hotelling's T-squared test (multivariate_ttest) — plus the multivariate assumption checks box_m (equal covariance) and multivariate_normality, for designs with several dependent variables.
Route psychology statistics requests to the smallest Pingouin workflow. Use when the user asks what analysis to run, gives a psychology design, asks to analyze data with Pingouin, or needs lower-token guidance before code generation.
Compute or generate Pingouin Bayes factors for t tests, correlations, and proportions (bayesfactor_ttest, bayesfactor_pearson, bayesfactor_binom), and read the BF10 already returned by pg.ttest and pg.corr.
Run or generate Pingouin code for categorical / contingency-table analyses — chi-square test of independence, McNemar's paired test, 2x2 crosstabs, and chi-square power — for psychology data with nominal variables.
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.
| name | pg-anova |
| description | Run or generate Pingouin code for one-way, factorial, repeated-measures, mixed, Welch ANOVA, ANCOVA, and follow-up pairwise tests. |
Use when the outcome is continuous and predictors are categorical factors, optionally with covariates or repeated measures.
Read:
../../references/supervision-gates.md../../references/pingouin-api-quickref.md../../references/apa-output-template.md if writing results.pg.anova(data=df, dv=..., between=..., detailed=True).pg.anova(data=df, dv=..., between=[...], detailed=True).pg.welch_anova.pg.rm_anova(..., within=..., subject=..., detailed=True).pg.mixed_anova.pg.ancova.pg.pairwise_tests with padjust.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)
np2.mixed_anova is for a specific mixed design; do not use it for arbitrary multi-level nesting.