Error and the Growth of Experimental Knowledge
509 pages
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12 line drawings, 6 tables
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6 x 9
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© 1996
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.
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.
London Schl Econ/Political Science: Lakatos Award in Philosophy of Science
Won
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
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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