Part 2: How to choose between the fixedeffect model There are two popular statistical models for metaanalysis, the fixedeffect model and the randomeffects model. Under the fixedeffect model we assume that there is one true effect size that underlies all the studies in the analysis, and that all differences in observed effects are due to sampling error. While we follow the practice of calling this a fixedeffect model, a more descriptive term would be a commoneffect model. In either case, we use the singular (effect) since there is only one true effect. By contrast, under the randomeffects model we allow that the true effect size might differ from study to study. For example, the effect size might be higher (or lower) in studies where the participants are older, or more educated, or healthier than in other studies, or when a more intensive variant of an intervention is used. The term "Random" reflects the fact that the studies included in the analysis are assumed to be a random sample of all possible studies that meet the inclusion criteria for the review. And we use the plural (effects) since we are working with multiple true effects. Part 1: Isquared is not an absolute measure of heterogeneity in a metaanalysis Researchers often use the I^{2} index to quantify the dispersion of effect sizes in a metaanalysis. Some suggest that I^{2} values of 25%, 50%, and 75%, correspond to small, moderate, and large amounts of heterogeneity. In fact though, I^{2} is a not a measure of absolute heterogeneity. Rather, it tells us what proportion of the observed variance reflects variance in true effect sizes rather than sampling error. This distinction between an absolute number and a proportion is fundamental to the correct interpretation of I^{2}. A metaanalysis with a low value of I^{2} could have only trivial heterogeneity but could also have substantial heterogeneity. Conversely, a metaanalysis with a high value of I^{2} could have substantial heterogeneity, but could also have only trivial heterogeneity. Our goal in this paper is to explain what I^{2} is, and how it should (and should not) be used in metaanalysis. 



