Common applications of the part correlation coefficient are in causal regression models and estimation of suppressor variable effects. However, there is no statistical test of the significance of the difference between a zero-order correlation and a part correlation, nor between a pair of part correlations. Hotelling's t is used for contrasting: (1) a zero-order predictor-criterion correlation, and a part correlation when the influence of a covariate is removed from predictor variance; (2) two part correlations based on mutual predictors and covariates; (3) two part correlations based on the same predictor but different covariates; and (4) two part correlations based on different predictors and the same covariate. Although Hotelling's t has enjoyed widespread citation in most research literature, criticisms have been raised because of its assumption of conditional normality and because, strictly speaking, statistical inference is limited to the sample values of the variables. However, Hotelling's t has greater familiarity and ubiquity, and its assumptions are no more restrictive than those associated with the Pearson product-moment correlation. (Author/RL)