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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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.
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.
Review and approve Pingouin psychology analyses before final reporting. Use after data screening or statistical output and before interpretation to catch common problems: wrong test, ignored repeated measures, missing assumption checks, multiple-comparison errors, unsupported causal claims, incomplete reporting, or output-format mismatches.
| name | pg-bayesian |
| description | 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. |
Use when the user wants Bayesian evidence (Bayes factors) alongside or instead of p-values, e.g. to quantify support for the null.
Read:
../../references/supervision-gates.md../../references/pingouin-api-quickref.md../../references/apa-output-template.md if writing results.BF10 column from pg.ttest(...); no extra call needed.BF10 from pg.corr(...).pg.bayesfactor_ttest(t, nx, ny=None, paired=False).pg.bayesfactor_pearson(r, n).pg.bayesfactor_binom(k, n, p).Bayes factor from a t test (BF10 is also already in the ttest table):
tt = pg.ttest(x, y, paired=False)
print({"BF10_from_table": float(tt["BF10"].iloc[0])})
bf = pg.bayesfactor_ttest(float(tt["T"].iloc[0]), nx=len(x), ny=len(y))
print({"BF10": round(float(bf), 3)})
Bayes factor for a correlation:
bf = pg.bayesfactor_pearson(r=0.30, n=60)
print({"BF10": round(float(bf), 3)})
Bayes factor for a proportion:
bf = pg.bayesfactor_binom(k=55, n=100, p=0.5)
print({"BF10": round(float(bf), 3)})
BF10 > 1 favors the alternative; BF10 < 1 favors the null. Rough labels: 1-3 anecdotal, 3-10 moderate, 10-30 strong, >30 very strong. BF01 = 1 / BF10.
r) used; the Bayes factor depends on it.