A primary goal of meta-analysis is to improve the estimation of treatment effects by pooling results of similar studies. This article explains how the most widely used method for pooling heterogeneous studies--the Der Simonian-Laird (DL) estimator--can produce biased estimates with falsely high precision. A classic example is presented to show that use of the DL estimator can lead to erroneous conclusions. Particular problems with the DL estimator are discussed, and several alternative methods for summarizing heterogeneous evidence are presented. The authors support replacing universal use of the DL estimator with analyses based on a critical synthesis that recognizes the uncertainty in the evidence,focuses on describing and explaining the probable sources of variation in the evidence, and uses random-effects estimates that provide more accurate confidence limits than the DL estimator.