Partial Correlation

Abstract

value used to represent the extent to which a null hypothesis is false. As an effect size, Cohen's d is typically used to represent the magnitude of differences between two (or more) groups on a given variable, with larger values representing a greater differentiation between the two groups on that variable. When comparing means in a scientific study, the reporting of an effect size such as Cohen's d is considered complementary to the reporting of results from a test of statistical significance. Whereas the test of statistical significance is used to suggest whether a null hypothesis is true (no difference exists between Populations A and B for a specific phenomenon) or false (a difference exists between [p. 181 ↓ ] Populations A and B for a specific phenomenon), the calculation of an effect size estimate is used to represent the degree of difference between the two populations in those instances for which the null hypothesis was deemed false. In cases for which the null hypothesis is false (i.e., rejected), the results of a test of statistical significance imply that reliable differences exist between two populations on the phenomenon of interest, but test outcomes do not provide any value regarding the extent of that difference. The calculation of Cohen's d and its interpretation provide a way to estimate the actual size of observed differences between two groups, namely, whether the differences are small, medium, or large.