Suppose we want to know not just whether instructional material matters, but how large its impact is. Sixty students learned with one of two materials, a printed book or an instructional video, and we recorded each student’s test_score afterward. A simple significance test tells us whether the groups differ, but reviewers increasingly want a standardized measure of the practical magnitude. We use an effect size analysis because it quantifies the standardized difference between the book and video groups, for example Cohen’s d, so we can communicate how meaningful the gap really is.

We measured the test-score difference between two teaching materials not just as “is it significant” but “how large is it” — as an effect size. Cohen’s d = -0.60 is a moderate effect. P-values inflate with sample size, but effect size is sample-independent and gives the practical importance of the real difference. In education, “how much difference does this material/method make” is critical for resource-allocation decisions; a moderate effect of d = 0.60 says the intervention provides a notable but not miraculous benefit. Reporting effect size is standard in publications.