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Improving statistical reasoning : theoretical models and practical implications

著者: Peter Sedlmeier
出版商: Mahwah, N.J. : Lawrence Erlbaum Associates, 1999.
版本/格式:   图书 : 英语查看所有的版本和格式
提要:
Improving Statistical Reasoning presents new studies of how people think and act on probabilistic information, focusing on the details of 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
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所有的著者/提供者: Peter Sedlmeier
ISBN: 0805832823 9780805832822
OCLC号码: 40544216
描述: x, 238 p. : ill. ; 24 cm.
内容: 1. Statistical Reasoning: How Good Are We? --
2. Are People Condemned to Remain Poor Probabilists? --
3. Prior Training Studies --
4. What Makes Statistical Training Effective? --
5. Conjunctive-Probability Training --
6. Conditional-Probability Training --
7. Bayesian-Inference Training I --
8. Bayesian-Inference Training II --
9. Sample-Size Training I --
10. A Flexible Urn Model --
11. Sample-Size Training II --
12. Implications of Training Results --
13. Associationist Models of Statistical Reasoning: Architectures and Constraints --
14. The PASS Model --
15. Statistical Reasoning: A New Perspective --
App. A. Variations of Bayesian Inference --
App. B. The Law of Large Numbers and Sample-Size Tasks
责任: Peter Sedlmeier.
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摘要:

Improving Statistical Reasoning presents new studies of how people think and act on probabilistic information, focusing on the details of 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 of the work are presented in a neural network model of human performance on statistical reasoning problems.

This book will be of interest to scholars in the fields of judgment and decision making and cognitive science, and to teachers of statistics and probabilistic reasoning.

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