Which of the following best describes the purpose of the chi-square test in statistics?

• A. It tests the fit between observed and expected frequencies.
• B. It measures the central tendency of a dataset.
• C. It assesses the dispersion of data around the mean.
• D. It estimates the probability of a health outcome.

Answer: (A)

Chi-square tests compare observed category counts to the counts you would expect under a stated hypothesis. This is done by tallying how many observations fall into each category and comparing those numbers to the expected frequencies, then measuring how far the observed data deviate from the expected using the sum of (observed − expected) squared over the expected for all categories. The resulting statistic helps determine whether any difference is likely due to chance or suggests a real divergence from the hypothesis.

This purpose centers on frequency data and patterns across categories, not on a dataset’s central value or its spread around that value. So it isn’t about measuring a mean or variance, and it isn’t about estimating the probability of a health outcome directly.