After decades of research,the social sciences still lack an integrative theoretical framework for analyzing and improving group judgment.I propose that connecting group decision making research with insights from machine learning and statistical theory could help us develop such a framework and answer these questions.The framework,based on decomposing the prediction error of group judgment into bias,variance,and covariance,makes it possible to determine when and why groups perform better than individuals and to devise new ways of improving the accuracy of group judgment.I use computer simulations to estimate the bias-variance profiles of cue- and exemplar-based strategies and the bias-variance-covariance profiles for groups of these strategies.The results show that exemplar-based strategies benefit the most from averaging,due to lower bias and higher variance compared to cue-based strategies.The performance of exemplar-based strategies is affected by the specifics of the similarity function and the rate at which similarity declines with distance.The insights from the bias-variance-covariance framework tell us that the success of group judgment depends on strategies the individual members are using (e.g.,low or high variance strategies),what information they are focusing on (e.g.,how much overlap there is between the information they are using),and how the individual assessments are used to reach a group decision (e.g.,averaging or relying on the perceived best member).Using the bias-variance-covariance framework to recognize and analyze these aspects of group judgments can help us understand what factors contribute to successful group decisions in a variety of real world contexts.