Chi-square tests are primarily used to assess what?

The primary use of chi-square tests is to analyze categorical data to determine if there is a statistically significant difference between the expected and observed frequencies in one or more categories. This test helps assess the association between two categorical variables by examining how likely it is that any observed difference between the categories occurred by chance.

In cases involving qualitative data, such as when classifications fall into groups like "yes" or "no," the chi-square test evaluates whether the distribution of those classifications differs from what would be expected under a specific null hypothesis, often implying that there is no association between the variables being tested.

For instance, in a study examining the relationship between smoking status (smoker versus non-smoker) and lung cancer diagnosis (yes versus no), a chi-square test would help to determine whether the frequency of lung cancer in smokers versus non-smokers deviates from what would be expected if there were no association between smoking and lung cancer.

In contrast, the other choices refer to statistical assessments that do not apply to the chi-square test. Continuous variables and correlation analysis typically require different statistical methods, such as t-tests or regression analysis. Trends over time would often be analyzed using methods suitable for time-series data, rather than a chi-square test. Hence, the