القائمة
Plan psychology study sample sizes or compute achieved power with Pingouin power functions for t tests, ANOVA, repeated-measures ANOVA, and correlations.
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استنادا إلى تصنيف SOC المهني
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-power |
| description | Plan psychology study sample sizes or compute achieved power with Pingouin power functions for t tests, ANOVA, repeated-measures ANOVA, and correlations. |
Use when the user asks for sample size, power, detectable effect, or planning assumptions.
Read:
../../references/supervision-gates.md../../references/pingouin-api-quickref.mdpg.power_ttest.pg.power_ttest2n.pg.power_anova.pg.power_rm_anova.pg.power_corr.Two-sample t test:
n = pg.power_ttest(d=0.5, n=None, power=0.80,
alpha=0.05, contrast="two-samples")
print(n)
Unequal groups:
power = pg.power_ttest2n(nx=30, ny=45, d=0.5, alpha=0.05)
print(power)
Correlation:
n = pg.power_corr(r=0.3, n=None, power=0.80, alpha=0.05)
print(n)
ANOVA:
n = pg.power_anova(eta_squared=0.06, k=3, n=None,
power=0.80, alpha=0.05)
print(n)
State: