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Error and the Growth of Experimental Knowledge

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ISBN-10: 0226511979

ISBN-13: 9780226511979

Edition: 1996

Authors: Deborah G. Mayo

List price: $140.00
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Description:

We may learn from our mistakes, but Deborah Mayo argues that, where experimental knowledge is concerned, we haven't begun to learn enough. Error and the Growth of Experimental Knowledge launches a vigorous critique of the subjective Bayesian view of statistical inference, and proposes Mayo's own error-statistical approach as a more robust framework for the epistemology of experiment. Mayo genuinely addresses the needs of researchers who work with statistical analysis, and simultaneously engages the basic philosophical problems of objectivity and rationality. Mayo has long argued for an account of learning from error that goes far beyond detecting logical inconsistencies. In this book, she…    
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Book details

List price: $140.00
Copyright year: 1996
Publisher: University of Chicago Press
Publication date: 7/15/1996
Binding: Hardcover
Pages: 509
Size: 6.00" wide x 9.00" long x 1.40" tall
Weight: 1.738
Language: English

Deborah G. Mayo is a professor in the Department of Philosophy at Virginia Polytechnic Institute and State University, known as Virginia Tech, and holds a visiting appointment in the Centre for the Philosophy of Natural and Social Science at the London School of Economics. She is the author of Error and the Growth of Experimental Knowledge, which in 1998 won the Lakatos Prize, awarded for the most outstanding contribution to philosophy of science during the previous six years. Professor Mayo coedited the volume Acceptable Evidence: Science and Values in Risk Management (1991, with R. Hollander) and has published numerous articles on the philosophy and history of science and foundations of…    

Preface
Learning from Error
Ducks, Rabbits, and Normal Science: Recasting the Kuhn's-Eye View of Popper
The New Experimentalism and the Bayesian Way
Duhem, Kuhn, and Bayes
Models of Experimental Inquiry
Severe Tests and Methodological Underdetermination
The Experimental Basis from Which to Test Hypotheses: Brownian Motion
Severe Tests and Novel Evidence
Hunting and Snooping: Understanding the Neyman-Pearson Predesignationist Stance
Why You Cannot Be Just a Little Bit Bayesian
Why Pearson Rejected the Neyman-Pearson (Behavioristic) Philosophy and a Note on Objectivity in Statistics
Error Statistics and Peircean Error Correction
Toward an Error-Statistical Philosophy of Science
References
Index