How do people judge which of 2 risks claims more lives per year? The authors specified 4 candidate mechanisms and tested them against people's judgments in 3 risk environments. Two mechanisms, availability by recall and regressed frequency, conformed best to people's choices. The same mechanisms also accounted well for the mapping accuracy of estimates of absolute risk frequencies. Their nearly indistinguishable level of performance is remarkable given their different assumptions about the underlying cognitive processes and the fact that they give rise to different expectations regarding the accuracy of people's inferences. The authors discuss this seeming paradox, the lack of impact of financial incentives on judgmental accuracy, and the dominant interpretation of inaccurate inferences in terms of biased information processing.

How likely is a diagnosis, given a particular medical test result? This probability can be determined by using Bayes's rule; however, previous research has shown that doctors often experience problems with Bayesian inferences. These findings illustrate the need to teach statistical reasoning in medical education. A new method of teaching Bayesian reasoning is representation learning: the key idea is to instruct medical students how to translate probability information into a representation that is easier to process, namely natural frequencies. This approach was implemented in a one-hour classroom tutorial to evaluate its effectiveness in this setting and compared with a traditional rule-learning approach. Evaluation took place two months after training by testing students' ability to correctly solve a Bayesian inference task with information represented as probabilities. While both approaches improved performance, almost three times as many students were able to profit from representation training as opposed to rule training.

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Objectives: Our aim is to provide an overview of key research findings from cognitive psychology regarding effective ways of communicating statistical information, and to point out the implications of these findings for genetic testing. Method: We review the literature on the presentation of statistical information in diagnostic test results, discuss various representations that invite misunderstandings, and propose alternative representations that foster understanding. Results: Single-event probabilities, conditional probabilities and relative risks are easily misunderstood. Specifying the class of events to which a probability refers and using natural frequency statements improve understanding. Conclusions: Cognitive psychology has identified simple and effective tools for improving statistical reasoning. They can help to improve the public’s understanding of diagnostic test results.