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## Abstract

When we speak about heterogeneity in a meta-analysis, our intent is usually to understand the substantive implications of the heterogeneity. If an intervention yields a mean effect size of 50 points, we want to know if the effect size in different populations varies from 40 to 60, or from 10 to 90, because this speaks to the potential utility of the intervention. While there is a common belief that the

*I*statistic provides this information, it actually does not. In this example, if we’re told that^{2}*I*is 50%, we have no way of knowing if the effects range from 40 to 60, or from 10 to 90, or across some other range. Rather, if we want to communicate the predicted range of effects, then we should simply report this range. This gives readers the information they think is being captured by^{2}*I*, and does so in a way that is concise and unambiguous.^{2}Original language | English |
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Pages (from-to) | 5-18 |

Number of pages | 14 |

Journal | Research Synthesis Methods |

Volume | 8 |

Issue number | 1 |

Early online date | 6 Jan 2017 |

DOIs | |

Publication status | Published - Mar 2017 |

## Keywords

- l-squared
- l2
- Heterogeneity
- Meta-analysis
- Prediction intervals
- Inconsistency

## Fingerprint Dive into the research topics of 'Basics of meta-analysis: <i>I<sup>2</sup></i> is not an absolute measure of heterogeneity'. Together they form a unique fingerprint.

## Projects

- 1 Finished