What is the main purpose of the Chi-squared test?

The main purpose of the Chi-squared test is to determine significant differences in categorical data sets. This statistical test assesses how likely it is that any observed difference between the expected frequencies and the actual observed frequencies in one or more categories is due to chance. It is particularly useful for situations where researchers want to analyze relationships between categorical variables or to test hypotheses about these variables.

In practice, the Chi-squared test helps to identify whether distributions of categorical data differ from one another under different conditions or populations. For example, researchers may want to analyze if the frequency of certain responses to a survey question significantly varies based on demographic factors. By establishing whether the differences are statistically significant, one can make informed decisions or draw conclusions based on the data.

While options discussing the analysis of symptoms, calculation of correlation coefficients, or evaluation of intervention effectiveness refer to different statistical methods or analyses, they do not capture the primary function of the Chi-squared test in examining categorical data relationships and differences.