What does an effect size describe?

What does an effect size describe?

• A. The magnitude of the treatment effect
• B. The probability of a false positive
• C. The mean difference between groups
• D. The sample size estimation

Answer: (A)

Effect size captures how large the treatment effect is, quantifying the magnitude of the difference the intervention produces. It matters because it tells you about practical significance and allows comparisons across studies, often using standardized metrics like Cohen’s d or odds ratios that account for variability. The probability of a false positive relates to p-values and significance testing, not how big the effect is. A mean difference is one way to express a difference in raw units but doesn’t standardize for variability or enable cross-study comparisons. Sample size estimation deals with planning and power, not the actual size of the observed effect. So the concept of effect size is about the magnitude of the treatment’s impact.

Effect size captures how large the treatment effect is, quantifying the magnitude of the difference the intervention produces. It matters because it tells you about practical significance and allows comparisons across studies, often using standardized metrics like Cohen’s d or odds ratios that account for variability. The probability of a false positive relates to p-values and significance testing, not how big the effect is. A mean difference is one way to express a difference in raw units but doesn’t standardize for variability or enable cross-study comparisons. Sample size estimation deals with planning and power, not the actual size of the observed effect. So the concept of effect size is about the magnitude of the treatment’s impact.