Evidence-appraisal glossary
Effect size
Effect size is a measure of how large an effect or difference is, separate from whether it is statistically significant. It answers how much, using metrics such as a mean difference, a risk ratio, or standardized measures like Cohen's d, so results can be judged for real-world importance.
Also called: ES, magnitude of effect, Cohen's d.
Effect size captures the magnitude of a relationship or difference, giving substance to a finding that a p-value alone cannot. It comes in many forms: absolute differences in means, relative measures like odds ratios and risk ratios, correlation coefficients, or standardized values such as Cohen's d that express a difference in standard-deviation units. Because effect size is largely independent of sample size, it lets a reader ask whether a result matters, not merely whether it is detectable. When reading a study, find the effect size and its confidence interval, and translate it into terms you understand before reacting to claims of significance. For example, an exercise program that improves a fitness score by a standardized effect of 0.1 has a small effect even if a large trial makes it statistically significant, while an effect of 0.8 would be substantial. Effect sizes also let different studies be compared and combined, which is why meta-analyses rely on them.
This is a plain-language methodology definition for reading research. It is general education, not medical advice.