Which statement best defines effect size?

Which statement best defines effect size?

• A. How much difference can be attributed to the independent variable
• B. P-value
• C. Sample size
• D. Mean difference

Answer: (A)

Effect size is about how large the observed effect is—the magnitude of the impact the independent variable has on the dependent variable. It captures the practical or real-world significance of the finding, not just whether an effect exists. It’s often reported as a standardized measure so you can compare results across studies and scales. For example, Cohen’s d expresses the difference between two group means in units of pooled standard deviation, so a bigger value means a stronger, more substantial effect regardless of sample size. P-values tell you whether an effect could be due to chance, but they don’t tell you how big the effect is. Sample size influences how precisely we estimate the effect and our power to detect it, not the size of the effect itself. A mean difference is one way to quantify an effect, but effect size is a broader concept—the standardized magnitude of the effect, which can take various forms (differences, correlations, odds ratios, etc.).

Effect size is about how large the observed effect is—the magnitude of the impact the independent variable has on the dependent variable. It captures the practical or real-world significance of the finding, not just whether an effect exists. It’s often reported as a standardized measure so you can compare results across studies and scales. For example, Cohen’s d expresses the difference between two group means in units of pooled standard deviation, so a bigger value means a stronger, more substantial effect regardless of sample size. P-values tell you whether an effect could be due to chance, but they don’t tell you how big the effect is. Sample size influences how precisely we estimate the effect and our power to detect it, not the size of the effect itself. A mean difference is one way to quantify an effect, but effect size is a broader concept—the standardized magnitude of the effect, which can take various forms (differences, correlations, odds ratios, etc.).