Includes bibliographical references and index.

Contents: Preface. Statistical Reasoning: How Good Are We? Are People Condemned to Remain Poor Probabilists? Prior Training Studies. What Makes Statistical Training Effective? Conjunctive-Probability Training. Conditional-Probability Training. Bayesian-Inference Training I. Bayesian-Inference Training II. Sample-Size Training I. A Flexible Urn Model. Sample-Size Training II. Implications of Training Results. Associationist Models of Statistical Reasoning: Architectures and Constraints. The PASS Model. Statistical Reasoning: A New Perspective. Appendices: Variations of Bayesian Inference. The Law of Large Numbers and Sample-Size Tasks. Is There a Future for Null-Hypothesis Testing in Psychology?

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.