In statistics, (between-) study heterogeneity is a phenomenon that commonly occurs when attempting to undertake a meta-analysis.
Study heterogeneity denotes the variability in outcomes that goes beyond what would be expected (or could be explained) due to measurement error alone.
[1] Meta-analysis is a method used to combine the results of different trials in order to obtain a quantitative synthesis.
However, the individual estimates of treatment effect will vary by chance; some variation is expected due to observational error.
[1] Different types of effect measures (e.g., odds ratio vs. relative risk) may also be more or less susceptible to heterogeneity.
It is difficult to establish the validity of any distributional assumption, and this is a common criticism of random effects meta-analyses.
In case the results to be amalgamated differ substantially (in their contexts or in their estimated effects), a derived meta-analytic average may eventually not correspond to a reasonable estimand.
[7] The heterogeneity variance is commonly denoted by τ², or the standard deviation (its square root) by τ.