This is an archived version. For the current version, please go to training.cochrane.org/handbook/current.

A very common and simple version of the meta-analysis procedure is commonly referred to as the **inverse-variance method**. This approach is implemented in its most basic form in RevMan, and is used behind the scenes in certain meta-analyses of both dichotomous and continuous data.

The inverse variance method is so named because the weight given to each study is chosen to be the inverse of the variance of the effect estimate (i.e. one over the square of its standard error). Thus larger studies, which have smaller standard errors, are given more weight than smaller studies, which have larger standard errors. This choice of weight minimizes the imprecision (uncertainty) of the pooled effect estimate.

A fixed-effect meta-analysis using the inverse-variance method calculates a weighted average as

,

where Y_{i} is the intervention effect estimated in the *i*th study, SE_{i} is the standard error of that estimate, and the summation is across all studies. The basic data required for the analysis are therefore an estimate of the intervention effect and its standard error from each study.