Abstract
In any meta-analysis it is important to report not only the mean effect size but also how the effect size varies across studies. A treatment that has a moderate clinical impact in all studies is very different than a treatment where the impact is moderate on average, but in some studies is large and in others is trivial (or even harmful). A treatment that has no impact in any studies is very different than a treatment that has no impact on average because it is helpful in some studies but harmful in others. The majority of meta-analyses use the I-squared index to quantify heterogeneity. While this practice is common it is nevertheless incorrect. I-squared does not tell us how much the effect size varies (except when I-squared is zero percent). The statistic that does convey this information is the prediction interval. It allows us to report, for example, that a treatment has a clinically trivial or moderate effect in roughly 10 % of studies, a large effect in roughly 50 %, and a very large effect in roughly 40 %. This is the information that researchers or clinicians have in mind when they ask about heterogeneity. It is the information that researchers believe (incorrectly) is provided by I-squared.