菜单
Run or generate Pingouin code for one-sample, independent, paired, Welch, and corrected pairwise mean comparisons in psychology studies.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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-mean-tests |
| description | Run or generate Pingouin code for one-sample, independent, paired, Welch, and corrected pairwise mean comparisons in psychology studies. |
Use for t tests and post hoc pairwise comparisons where the dependent variable is approximately continuous.
Read:
../../references/supervision-gates.md../../references/pingouin-api-quickref.md../../references/apa-output-template.md if writing results.pg.ttest(x, y=<value>).pg.ttest(x, y, paired=False, correction="auto").pg.ttest(x, y, paired=True).pg.pairwise_tests.pg.pairwise_tests(..., parametric=False).holm for post hoc families unless the user specifies another correction.two-sided.Independent t test:
x = df.loc[df["group"].eq("A"), "score"]
y = df.loc[df["group"].eq("B"), "score"]
res = pg.ttest(x, y, paired=False, correction="auto",
alternative="two-sided", confidence=0.95).round(3)
pg.print_table(res)
Paired t test from wide columns:
res = pg.ttest(df["pre"], df["post"], paired=True,
alternative="two-sided", confidence=0.95).round(3)
pg.print_table(res)
Pairwise tests:
res = pg.pairwise_tests(data=df, dv="score", between="group",
parametric=True, padjust="holm",
effsize="hedges").round(3)
pg.print_table(res)
Repeated pairwise tests:
res = pg.pairwise_tests(data=df, dv="score", within="condition",
subject="id", padjust="holm",
effsize="hedges").round(3)
Inspect res.columns before writing prose. Expected t-test columns often include T, dof, p-val, CI95%, cohen-d, BF10, and power.
pairwise_ttests.