The Chi-square test is a non-parametric hypothesis testing procedure used to determine statistical independence between two categorical variables within statistics and data analysis. The core principle relies on comparing observed frequencies in contingency table cells against expected frequencies under the assumption of null distribution, subject to the constraint that all expected cell counts must exceed five. Significance is established by calculating a test statistic using deviations standardized by expected values and evaluating it against critical thresholds determined by degrees of freedom at a specific significance level (alpha), without accounting for ordinal rankings within categories.
Prerequisite chain context: requires Degrees of Freedom Calculation for k x n Frequency Tables.