The Chi-Square test evaluates whether proportions of subjects in each category are independent of each other. The Chi-Square test is used to evaluate independence between which type of variables?

The Chi-Square test evaluates whether proportions of subjects in each category are independent of each other. The Chi-Square test is used to evaluate independence between which type of variables?

• A. Categorical Variables
• B. Continuous Variables
• C. Ordinal Variables
• D. Interval Data

Answer: (A)

The chi-square test of independence is built for categorical data. It asks whether the distribution of counts across categories of one variable differs depending on the category of another variable. Data are arranged in a contingency table, and observed counts are compared to expected counts under the assumption of independence (expected = row total × column total / grand total). A significant result suggests the variables are associated rather than independent. This test is appropriate for nominal or ordinal categories because both variables are treated as categories; continuous measurements, like height or score, aren’t analyzed for independence with chi-square unless they’re first converted into categories. For continuous data, other methods such as correlation or regression are used to assess association.

The chi-square test of independence is built for categorical data. It asks whether the distribution of counts across categories of one variable differs depending on the category of another variable. Data are arranged in a contingency table, and observed counts are compared to expected counts under the assumption of independence (expected = row total × column total / grand total). A significant result suggests the variables are associated rather than independent. This test is appropriate for nominal or ordinal categories because both variables are treated as categories; continuous measurements, like height or score, aren’t analyzed for independence with chi-square unless they’re first converted into categories. For continuous data, other methods such as correlation or regression are used to assess association.