The Chi-Squared test bins observations into categories (e.g., deciles) and compares the observed frequencies to the expected frequencies. Since it relies on grouping data into bins, it handles large sample sizes extremely efficiently with low computational intensity compared to tests that evaluate the entire empirical distribution function (like Kolmogorov-Smirnov, Anderson-Darling, and Cramér-von Mises), which typically require sorting the data—a computationally heavy task (O(N log N) time complexity) for very large datasets. Although it loses some information due to binning, the Chi-Squared test is prioritized when sample size and computational resources are constraints.