Error and the Growth of Experimental KnowledgeUniversity of Chicago Press, 15 ביולי 1996 - 493 עמודים 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. |
תוכן
Learning from Error | 1 |
Ducks Rabbits and Normal Science Recasting the KuhnsEye View of Popper | 21 |
The New Experimentalism and the Bayesian Way | 57 |
Duhem Kuhn and Bayes | 102 |
Models of Experimental Inquiry | 128 |
Severe Tests and Methodological Underdetermination | 174 |
The Experimental Basis from Which to Test Hypotheses Brownian Motion | 214 |
Severe Tests and Novel Evidence | 251 |
Hunting and Snooping Understanding the NeymanPearson Predesignationist Stance | 294 |
Why You Cannot Be Just a Little Bit Bayesian | 319 |
Why Pearson Rejected the NeymanPearson Behavioristic Philosophy and a Note on Objectivity in Statistics | 361 |
Error Statistics and Peircean Error Correction | 412 |
Toward an ErrorStatistical Philosophy of Science | 442 |
References | 465 |
| 481 | |
מהדורות אחרות - הצג הכל
Error and the Growth of Experimental Knowledge <span dir=ltr>Deborah G. Mayo</span> תצוגה מקדימה מוגבלת - 1996 |
Error and the Growth of Experimental Knowledge <span dir=ltr>Deborah G. Mayo</span> תצוגה מקדימה מוגבלת - 1996 |
מונחים וביטויים נפוצים
accept actual alternative appraisal argue argument from error assign Bayes's theorem Bayesian Binomial Brownian motion calculated chance chapter correlation criticism data models deflection degrees of belief discussion distribution E. S. Pearson eclipse effect ence error probabilities error statistician error statistics estimate evidence example experiment experimental knowledge experimental model experimental testing factors Giere given Howson and Urbach hypothe hypothesis H induction inquiry Kuhn Kuhn's likelihood likelihood principle logical mean ment methods Neyman non-Bayesian normal science normal testing novelty NP tests null hypothesis observed outcome paradigm parameter passed a severe Pearson Peirce Peirce's Perrin philosophy of science Popper Popperian posterior probability predesignation prediction primary prior problem question random reject H relative frequency reliable requirement result sample scientific inference scientists severe test significance level specific standard error statistics statistical inference statistical tests statistically significant stopping rule success theory tion trials true type I error use-constructed violating
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What Is This Thing Called Science? (Third Edition) <span dir=ltr>Alan F. Chalmers</span> אין תצוגה מקדימה זמינה - 1999 |
Info-Gap Decision Theory: Decisions Under Severe Uncertainty <span dir=ltr>Yakov Ben-Haim</span> תצוגה מקדימה מוגבלת - 2006 |