Related Experiment Video
Updated: Jun 27, 2026

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
Published on: October 25, 2011
Undue reliance on I(2) in assessing heterogeneity may mislead
Gerta Rücker1, Guido Schwarzer, James R Carpenter
1Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Germany. ruecker@imbi.uni-freiburg.de
Background:
The heterogeneity statistic I(2), interpreted as the percentage of variability due to heterogeneity between studies rather than sampling error, depends on precision, that is, the size of the studies included.
Methods:
Based on a real meta-analysis, we simulate artificially 'inflating' the sample size under the random effects model. For a given inflation factor M = 1, 2, 3,... and for each trial i, we create a M-inflated trial by drawing a treatment effect estimate from the random effects model, using s(i)(2)/M as within-trial sampling variance.
Results:
As precision increases, while estimates of the heterogeneity variance tau(2) remain unchanged on average, estimates of I(2) increase rapidly to nearly 100%. A similar phenomenon is apparent in a sample of 157 meta-analyses.
Conclusion:
When deciding whether or not to pool treatment estimates in a meta-analysis, the yard-stick should be the clinical relevance of any heterogeneity present. tau(2), rather than I(2), is the appropriate measure for this purpose.
Related Concept Videos
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Test for Homogeneity
Errors In Hypothesis Tests
Confounding in Epidemiological Studies
Bias in Epidemiological Studies
Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences