Pearson's r is best described as:

• A. A parametric measure of correlation based on variance in two sets of scores
• B. A non-parametric rank-based correlation
• C. A chi-square statistic
• D. A regression slope

Answer: (A)

Pearson's r captures the strength and direction of the linear relationship between two continuous variables. It’s a parametric measure because it relies on statistical parameters like means, variances, and covariances, using the actual score values to compute the relationship. The calculation standardizes covariance by the product of the variables’ standard deviations, which ties it directly to variance terms in each data set.

This is different from a non-parametric, rank-based correlation (which uses just the order of the data, not the actual scores). It’s also not a chi-square statistic, which tests things like independence or goodness-of-fit with categorical data. And while there is a connection between r and the slope in simple regression, r itself is not the regression slope—the slope describes how one variable changes with the other, whereas r describes the strength and direction of the linear association.