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.
"…the book is full of interesting research on reasoning."
"Overall, this book does several things well, and thus is an important contribution to the literature and potentially relevant to a number of different audiences. Sedlmeier provides an excellent review of the literature on errors in statistical reasoning and prior training studies, and thus the book is a useful introduction to people new to the field. Second, the training studies are impressive and insightful, and they might serve as fodder for much future research on training in statistical reasoning. Finally, as Sedlmeier points out, the social implications of teaching statistical reasoning skills are enormous. People use statistics to sell products to you, to convince people to vote for certain candidates or to choose a course of treatment for a disease. Teaching us how to train people to reason about statics is the most important contribution of this book."
—Applied Cognitive Psychology
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?