What does the Chi-Square Test primarily assess in hypothesis testing?

The Chi-Square Test primarily assesses whether the observed data matches what is expected under a certain hypothesis, focusing on categorical variables. This statistical test compares the frequencies of occurrences in different categories to determine if there is a significant difference between the expected and observed counts.

For example, in a goodness-of-fit test, researchers might expect certain outcomes based on a theoretical distribution. The Chi-Square Test calculates how far the observed outcomes deviate from these expected counts. If the deviation is greater than what might be expected by chance, the hypothesis might be rejected, indicating that the data does not fit the expected distribution.

While correlation, differences in means, and variability are important concepts in statistics, they are not the main focus of the Chi-Square Test. It specifically targets the alignment of observed data with theoretical expectations rather than measuring relationships between variables, comparing means, or assessing sample variability.