In empirical studies the notion statistically significant is often used. This should not be mingled with significance in the usual meaning: statistical significance indicates only that the effect is probably not an artifact caused by too small sample size. It is an information about the quality of the research, not about the importance of the effect.
But if we want to know how important the effect is, we need another measure: effect size. For example, in the case of harm caused by sexual abuse, the effect size is small - only about 1%.
Here, a popular explanation:
The common value derived from each study in the meta-analyses we’ll be discussing is called an effect size, which tells you how big the difference is between CSA and control subjects in terms of their adjustment. This is different from saying that the two groups showed a statistically significant difference, because such a difference could be very small or quite big. The effect size tells us whether the difference is small or big. If you save one guilder at store A compared to store B on a 1000 guilder item, there’s a difference, but it’s quite small. If you save 200 guilders, then that’s something. As a shopper, you want to know how much you’ll save by going to store A, not simply whether you’ll save. This is the spirit of effect size analysis.
For ease of presentation, given that many of you are not familiar with statistics, we will report effect sizes in the following way. Imagine that we have a group of people, some of whom had CSA and some of whom did not. Now, you can imagine that there is a lot of variation in both groups in terms of how well the different individuals are adjusted. Some will be very well adjusted, others moderately so, others not too well, and a few will be seriously maladjusted. If CSA had a very strong effect on adjustment, then CSA should account for at least 50% of the adjustment variability among all of the subjects. If CSA had a strong effect, it should account for at least 25%. If CSA had a medium effect, it should account for about 10%. And if CSA had only a small effect, it should account for about 1% of the adjustment variability.