What is an effect size and why is it important beyond p-values?

• A. An effect size is a test statistic used in hypothesis testing.
• B. A probability value indicating statistical significance.
• C. A measure of sample size.
• D. A quantitative measure of the magnitude of an effect; helps assess practical significance.

Answer: (D)

An effect size is a numerical gauge of how big an observed effect is, not just whether an effect exists. It tells you the magnitude in a standardized way so you can judge practical importance, not just statistical significance. A p-value only tells you how likely the observed data would be if there were no real effect, and it can be swayed a lot by sample size and data variability. That means you can get a tiny but statistically significant difference with a large sample, or miss a meaningful difference with a small one. The effect size separates the question of “does it exist?” from “how big is it, in real terms?” This helps compare results across studies and informs power analyses for planning future research. Common metrics include standardized mean differences like Cohen’s d, correlation coefficients like r, and ratios such as odds or risk for binary outcomes. In short, it communicates the real-world importance of the finding, complementing the p-value rather than replacing it.