Today’s lecture focused on the Chi-Square test, a robust statistical tool used for examining relationships between categorical variables. It’s especially useful for evaluating if two categorical variables are independent or associated. This involves comparing actual data in a contingency table with expected data assuming independence. There are several types of Chi-Square tests, each with a specific function. The Chi-Square Test for Independence is used to determine if there’s a significant link between two variables, helping to identify dependencies. The Chi-Square Goodness-of-Fit Test checks if observed data matches a particular distribution, like normal or uniform, which is useful for evaluating model fit. Finally, the Chi-Square Test for Homogeneity investigates whether the distribution of a categorical variable is consistent across different groups or populations. These varied applications provide a thorough understanding of the Chi-Square test’s utility in analyzing and interpreting categorical data across different statistical scenarios.