The mathematical representation of Brunswik's lens model has been used extensively to study human judgment and provides a unique opportunity to conduct a meta-analysis of studies that covers roughly five decades. Specifically, we analyze statistics of the "lens model equation" (Tucker, 1964) associated with 249 different task environments obtained from 86 papers. In short, we find - on average - fairly high levels of judgmental achievement and note that people can achieve similar levels of cognitive performance in both noisy and predictable environments. We further identify and estimate the effects of task characteristics that influence judgment (numbers and types of cues, inter-cue redundancy, function forms and cue weights in the ecology, laboratory versus field studies, and experience with the task). A detailed analysis of learning studies reveals that the most effective form of feedback is information about the task. We also analyze empirically when the application of bootstrapping - or replacing judges by their linear models - is advantageous. We conclude by indicating shortcomings of the kinds of studies conducted to date, limitations in the lens model methodology, and possibilities for future research.