Awards

• Winner of Lakatos Prize 1998
• Winner of Lakatos Prize 1998.

Overview

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 presents her complete program for how we learn about the world by being ""shrewd inquisitors of error, white gloves off."" Her tough, practical approach will be important to philosophers, historians, and sociologists of science, and will be welcomed by researchers in the physical, biological, and social sciences whose work depends upon statistical analysis.

Full Product Details

Author:   Deborah G. Mayo
Publisher:   The University of Chicago Press
Imprint:   University of Chicago Press
Edition:   2nd ed.
Dimensions:   Width: 1.60cm , Height: 0.40cm , Length: 2.30cm
Weight:   0.794kg
ISBN:  

9780226511979

ISBN 10:   0226511979
Pages:   509
Publication Date:   15 July 1996
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

Preface 1: Learning from Error 2: Ducks, Rabbits, and Normal Science: Recasting the Kuhn's-Eye View of Popper 3: The New Experimentalism and the Bayesian Way 4: Duhem, Kuhn, and Bayes 5: Models of Experimental Inquiry 6: Severe Tests and Methodological Underdetermination 7: The Experimental Basis from Which to Test Hypotheses: Brownian Motion 8: Severe Tests and Novel Evidence 9: Hunting and Snooping: Understanding the Neyman-Pearson Predesignationist Stance 10: Why You Cannot Be Just a Little Bit Bayesian 11: Why Pearson Rejected the Neyman-Pearson (Behavioristic) Philosophy and a Note on Objectivity in Statistics 12: Error Statistics and Peircean Error Correction 13: Toward an Error-Statistical Philosophy of Science References Index

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