A statistical tool employed in conjunction with Analysis of Variance (ANOVA) procedures quantifies the magnitude of the difference between group means. This measurement provides information beyond the statistical significance (p-value) determined by the ANOVA test itself. For instance, while ANOVA might reveal that significant differences exist between the average scores of three treatment groups, a calculation of effect size clarifies whether those differences are substantial from a practical or clinical perspective. Common metrics derived include Cohen’s d, eta-squared (), and omega-squared (), each offering a standardized means to represent the proportion of variance in the dependent variable that is explained by the independent variable.
The determination of the practical significance of research findings is greatly enhanced through the use of these metrics. ANOVA, while valuable for identifying statistically significant differences, does not inherently indicate the degree to which the independent variable influences the dependent variable. Historically, statistical significance alone was often used to judge the value of research. However, researchers increasingly recognize that a small p-value can result from large sample sizes, even when the observed effect is trivial. Therefore, these measurements offer vital information for interpreting the real-world implications of research findings and conducting meta-analyses across multiple studies.
