This book focuses on how statistical reasoning works and on training programs that can exploit people's natural cognitive capabilities to improve their statistical reasoning. Training programs that take into account findings from evolutionary psychology and instructional theory are shown to have substantially larger effects that are more stable over time than previous training regimens. The theoretical implications are traced in a neural network model of human performance on statistical reasoning problems. This book apppeals to judgment and decision making researchers and other cognitive scientists, as well as to teachers of statistics and probabilistic reasoning.

From childhood, each of us develops our own personal set of theories and beliefs about the world in which we live. Given the impossibility of knowing about every event that can ever take place, we use cognitive short cuts to try to predict and make sense of the world around us. One of the fundamental pieces of information we use to predict future events, and make sense of past events, is 'frequency' - how often has such an event happened to us, or how often have we observed a particular event? With such information we will make inferences about the likelihood of its future appearance. We will make judgements, assess risk, or even consumer decisions, on the basis of this information. We also form associations between events that frequently occur together, and even (often incorrectly) attribute causality between one event and the other as a result of their simultaneous appearance. How is it though that we process such information? How does our brain deal with information on frequencies? How does such information influence our behaviour, beliefs, and judgements? Important new findings on this topic have come from research within both social and cognitive psychology, though until now, never brought together in a single volume. This is the first book to bring together two disparate literatures on this topic - drawing on research from both cognitive psychology and social psychology. Including contributions from world leaders in the field, this is a timely, and long overdue volume on this topic.

This book focuses on how statistical reasoning works and on training programs that can exploit people's natural cognitive capabilities to improve their statistical reasoning. Training programs that take into account findings from evolutionary psychology and instructional theory are shown to have substantially larger effects that are more stable over time than previous training regimens. The theoretical implications are traced in a neural network model of human performance on statistical reasoning problems. This book apppeals to judgment and decision making researchers and other cognitive scientists, as well as to teachers of statistics and probabilistic reasoning.