pg-categoricallisted

# PG Categorical Use when both variables are categorical (nominal) and the question is association or change in proportions. ## Load Read: - `../../references/supervision-gates.md` - `../../references/pingouin-api-quickref.md` - `../../references/apa-output-template.md` if writing results. ## Decision Rules - Association between two independent categorical variables (any R x C) -> `pg.chi2_independence(data, x, y)`. - Change in a binary outcome for the same participants (paired 2x2) -> `pg.chi2_mcnemar(data, x, y)`. - Just the 2x2 table from two binary columns -> `pg.dichotomous_crosstab(data, x, y)`. - Sample size / power for a chi-square test -> `pg.power_chi2(dof, w, n, power, alpha)`. ## Required Inputs - Two categorical columns (row and column variables). - Whether observations are independent (chi-square) or paired within participants (McNemar). - For McNemar/crosstab: columns must be dichotomous (0/1 or two levels). - For power: effect size `w` (Cohen), degrees of freedom, and the unknown to solve (set to `None`). ## Code Patterns Chi-square test of independence (returns a 3-tuple): ```python 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): ```python observed, stats = pg.chi2_mcnemar(data=df, x="before", y="after") pg.print_table(stats.round(3)) ``` 2x2 crosstab: ```python ct = pg