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title: "4.3.10.2 Quantification of the statistical heterogeneity: I squared"
canonical: "https://jbi-global-wiki.refined.site/space/MANUAL/355828547/4.3.10.2%20Quantification%20of%20the%20statistical%20heterogeneity%3A%20I%20squared"
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The I square statistic (I 2 ) represents the percentage of the variability in effect estimates that is due to heterogeneity (Deeks et al 2008). I 2  is the proportion of observed dispersion of results from different studies included in a meta-analysis that is real, rather than spurious (Borenstein et al 2009). The I 2  index can be interpreted as the percentage of the total variability in a set of effect sizes due to true heterogeneity (between-studies variability) (Huedo-Medina et al 2006). If I 2  = 0%, this indicates that all variability in effect size estimates is due to sampling error within studies. If I 2 = 50%, it indicates that half of the total variability among effect sizes is caused not by sampling error, but by true heterogeneity between studies (Huedo-Medina et al 2006).  I 2  is a percentage and its values lie between 0% and 100% (Higgins et al 2003). A value of 0% indicates no observed heterogeneity, and larger values show increasing heterogeneity (Higgins et al 2003). One proposed suggestion was to consider as low, moderate, and high heterogeneity for I 2  values of 25%, 50%, and 75% (Higgins et al 2003). Another guide to interpretation was proposed: 0% to 40% might not be important; 30% to 60% may represent moderate heterogeneity; 50% to 90% may represent substantial heterogeneity; 75% to 100% considerable heterogeneity (Deeks et al 2008). Authors of the guide mention that careful interpretation of the value of I 2  depends on magnitude and direction of effects and strength of evidence for heterogeneity (Deeks et al 2008). With a small number of studies (< 20) and/or average sample size (N <80) the statistical power for I 2  procedures is less than the usually recommended minimum value of 0.8 (Huedo-Medina et al 2006). With a small number of studies (< 20), both the I 2  confidence interval and the Q test should be interpreted very cautiously (Huedo-Medina et al 2006).