This article demonstrates that the weighting-according-to-the-variance method may introduce biases in meta-analyses of binary outcomes. The weighting favors studies that have certain frequencies of outcome events and weights given to studies of the same size may differ tens to thousands of times merely because of variations in the frequency. It also applies different standards to different measures of effect. Thus, the weighting may distort the combined result or even lead to contradictory conclusions when different measures of effect are used. Generally, the bias is more likely to arise when the effect is heterogeneous across the combined trials, the trials are conducted in populations of highly varied risks, the relative risk is used as the effect measure, the effect to be combined is small, any of the trials falls beyond the risk range of 20% and 80%, and/or the number of trials is small. Suggestions for detection and control of the bias are also given.