Which of the following is an example of an effect size?

• A. P-value
• B. Confidence interval
• C. Cohen's d
• D. Standard error

Answer: (C)

An effect size measures how large the observed difference or relationship is, independent of sample size. Cohen's d is a classic example because it quantifies the magnitude of the difference between two groups in standard deviation units. Specifically, you take the difference between the group means and divide by a pooled standard deviation, which puts the result on a common scale. This tells you not just that groups differ, but how substantial that difference is in practical terms (for example, a d of around 0.2 is small, about 0.5 is medium, and 0.8 or larger is large).

The other options don’t themselves quantify magnitude. A p-value indicates how unlikely the observed data would be if there were no real effect, but it doesn't describe how big the effect is. A confidence interval gives a range of plausible values for the effect size but isn’t a single measure of magnitude. A standard error measures the precision of an estimate (how much the estimate would vary across samples), not the size of the effect. Thus, Cohen's d is the example that represents an effect size.