Error and the Growth of Experimental KnowledgeWe 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. |
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Inhalt
Learning from Error | 1 |
Recasting the KuhnsEye | 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 |
Brownian | 214 |
Severe Tests and Novel Evidence | 251 |
Understanding the NeymanPearson | 294 |
Why You Cannot Be Just a Little Bit Bayesian | 319 |
Why Pearson Rejected the NeymanPearson Behavioristic | 361 |
Error Statistics and Peircean Error Correction | 412 |
Toward an ErrorStatistical Philosophy of Science | 442 |
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accept according actual allows alternative answer approach argue argument assign assumptions Bayesian Brownian calculated called canonical chance chapter claim consider constructed correct criticism discussion distribution effect equals error probabilities estimate evidence example existence expected experiment experimental explain fact factors fail false follows give given grounds hypothesis hypothesis H important indicate induction inference inquiry interest interpretation kind knowledge known Kuhn learning less logical matter mean measure methods normal null objection observed outcome particular passing Pearson Peirce philosophy Popper positive possible prediction primary prior problem procedure question random reason refer regard reject reliable requirement result rule sample scientific scientists severe test significance specific standard statistical statistically significant step subjective success suppose task theory tion trials true violating
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