In this unit, students explore Inference for Categorical Data using Chi-Square (χ²) tests. This chapter bridges the gap between simple proportions and complex datasets, allowing students to determine if an observed distribution matches a theoretical model or if two categorical variables are related.

Students will master the three primary types of inference procedures: Chi-Square Goodness of Fit (one variable, one population), Chi-Square Test for Homogeneity (one variable, multiple populations), and the Chi-Square Test for Independence (two variables, one population).

Key concepts include calculating Expected Counts, determining Degrees of Freedom, and checking the Large Counts Condition (all expected counts ≥ 5). Students will also learn to interpret the P-value in the context of the χ² distribution and perform follow-up analysis to identify which categories contribute most to a significant result.