Peter Sedlmeier - Improving statistical reasoning: theoretical models and practical implications
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Improving statistical reasoning: theoretical models and practical implications
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This book focuses on how statistical reasoning works and on training programs that can exploit peoples 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.
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Improving Statistical Reasoning : Theoretical Models and Practical Implications
author
:
Sedlmeier, Peter.
publisher
:
Lawrence Erlbaum Associates, Inc.
isbn10 | asin
:
0805832823
print isbn13
:
9780805832822
ebook isbn13
:
9780585115153
language
:
English
subject
Mathematical statistics.
publication date
:
1999
lcc
:
QA276.12.S424 1999eb
ddc
:
519.5
subject
:
Mathematical statistics.
Page iii
Improving Statistical Reasoning
Theoretical Models and Practical Implications
Peter Sedlmeier University of Paderborn
LAWRENCE ERLBAUM ASSOCIATES, PUBLISHERS 1999 Mahwah, New Jersey London
Page iv
The final camera copy for this work was prepared by the author, and therefore the publisher takes no responsibility for consistency or correctness of typographical style. However, this arrangement helps to make publication of this kind of scholarship possible.
Copyright 1999 by Lawrence Erlbaum Associates, Inc.
All rights reserved. No part of this book may be reproduced in any form, by photostat, microfilm, retrieval system, or any other means, without prior written permission of the publisher.
Library of Congress Cataloging-in-Publication Data
Sedlmeier, Peter. Improving statistical reasoning: theoretical models and practical implications / Peter Sedlmeier. p. cm. Includes bibliographical references and index. ISBN 0-8058-3282-3 (c: alk. paper) 1. Mathematical statistics. I. Title. QA276.12.S424 1999 519.5 dc2198-35642 CIP
Books published by Lawrence Erlbaum Associates are printed on acid-free paper, and their bindings are chosen for strength and durability.
Printed in the United States of America 10 9 8 7 6 5 4 3 2 1
Page v
Contents
Preface
vii
1 Statistical Reasoning: How Good Are We?
1
2 Are People Condemned to Remain Poor Probabilists?
29
3 Prior Training Studies
43
4 What Makes Statistical Training Effective?
62
5 Conjunctive-Probability Training
67
6 Conditional-Probability Training
80
7 Bayesian-Inference Training I
92
8 Bayesian-Inference Training II
105
9 Sample-Size Training I
111
10 A Flexible Urn Model
121
11 Sample-Size Training II
128
12 Implications of Training Results
140
13 Associationist Models of Statistical Reasoning: Architectures and Constraints
150
14 The PASS Model
165
15 Statistical Reasoning: A New Perspective
189
Appendix A: Variations of Bayesian Inference
198
Appendix B: The Law of Large Numbers and Sample-Size Tasks
202
Appendix C: Is There a Future For Null-Hypothesis Testing in Psychology?
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