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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.
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.
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 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-categorical |
| description | 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. |
Use when both variables are categorical (nominal) and the question is association or change in proportions.
Read:
../../references/supervision-gates.md../../references/pingouin-api-quickref.md../../references/apa-output-template.md if writing results.pg.chi2_independence(data, x, y).pg.chi2_mcnemar(data, x, y).pg.dichotomous_crosstab(data, x, y).pg.power_chi2(dof, w, n, power, alpha).w (Cohen), degrees of freedom, and the unknown to solve (set to None).Chi-square test of independence (returns a 3-tuple):
expected, observed, stats = pg.chi2_independence(data=df, x="group", y="response")
pg.print_table(stats.round(3)) # read the "pearson" row
McNemar paired test (binary 0/1 columns; returns observed table + stats):
observed, stats = pg.chi2_mcnemar(data=df, x="before", y="after")
pg.print_table(stats.round(3))
2x2 crosstab:
ct = pg.dichotomous_crosstab(data=df, x="cond_a", y="cond_b")
print(ct)
Chi-square power / sample size:
power = pg.power_chi2(dof=1, w=0.3, n=100, alpha=0.05) # solve n via n=None, power=0.8
print({"power": round(float(power), 3)})
chi2_independence stats rows include pearson; columns are test, lambda, chi2, dof, pval, cramer (effect size), power. Report Cramér's V, not just chi-square. chi2_mcnemar stats give chi2, dof, p_approx, p_exact; prefer p_exact for small discordant counts.