Which statement about effect size is true?

• A. It is independent of sample size.
• B. It is the same as the p-value.
• C. It only measures variance within a group.
• D. It increases with sample size in all cases.

Answer: (A)

Effect size tells you how big the observed effect is, in a standardized way, so you can judge practical significance rather than just whether something happened. The key idea is that the magnitude of the effect reflects the true difference or association in the population, not how many people you happened to sample. So, even if you collect more data, the size of the effect itself stays the same (though your estimate becomes more precise with more data).

That’s why this statement is true: effect size is independent of sample size. What changes with more data is the precision of that estimate and the p-value from a statistical test, which can become significant for very small effects when the sample is large. It’s also important to note that effect size isn’t the same as a p-value, and it isn’t limited to measuring variance within a single group. It can quantify between-group differences or the strength of a relationship, and its magnitude does not automatically increase as sample size grows.