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181   $ctxt
182   $cc
183   $acr
200 10$aError and the growth of experimental knowledge$b[electronic resource] /$fDeborah G. Mayo
210   $aChicago $cUniversity of Chicago Press$d1996
215   $a1 online resource (512 p.)
225 1 $aScience and its conceptual foundations
300   $aDescription based upon print version of record.
311   $a0-226-51198-7 
311   $a0-226-51197-9 
320   $aIncludes bibliographical references (p. 465-480) and index.
327   $tFrontmatter -- $tContents -- $tPreface -- $t1. Learning from Error -- $t2. Ducks, Rabbits, and Normal Science: Recasting the Kuhn's-Eye View of Popper -- $t3. The New Experimentalism and the Bayesian Way -- $t4. Duhem, Kuhn, and Bayes -- $t5. Models of Experimental Inquiry -- $t6. Severe Tests and Methodological Underdetermination -- $t7. The Experimental Basis from Which to Test Hypotheses: Brownian Motion -- $t8. Severe Tests and Novel Evidence -- $t9. Hunting and Snooping: Understanding the Neyman-Pearson Predesignationist Stance -- $t10. Why You Cannot Be Just a Little Bit Bayesian -- $t11. Why Pearson Rejected the Neyman-Pearson (Behavioristic) Philosophy and a Note on Objectivity in Statistics -- $t12. Error Statistics and Peircean Error Correction -- $t13. Toward an Error-Statistical Philosophy of Science -- $tReferences -- $tIndex
330   $aWe may learn from our mistakes, but Deborah Mayo argues that, where experimental knowledge is concerned, we haven't begun to learn enough. Error and the Growth of Experimental Knowledge launches a vigorous critique of the subjective Bayesian view of statistical inference, and proposes Mayo's own error-statistical approach as a more robust framework for the epistemology of experiment. Mayo genuinely addresses the needs of researchers who work with statistical analysis, and simultaneously engages the basic philosophical problems of objectivity and rationality. Mayo has long argued for an account of learning from error that goes far beyond detecting logical inconsistencies. In this book, she presents her complete program for how we learn about the world by being "shrewd inquisitors of error, white gloves off." Her tough, practical approach will be important to philosophers, historians, and sociologists of science, and will be welcomed by researchers in the physical, biological, and social sciences whose work depends upon statistical analysis.
410  0$aScience and its conceptual foundations.
606   $aError analysis (Mathematics)
606   $aBayesian statistical decision theory
606   $aScience$xPhilosophy
608   $aElectronic books.
615  0$aError analysis (Mathematics)
615  0$aBayesian statistical decision theory.
615  0$aScience$xPhilosophy.
676   $a001.4/34
700   $aMayo$b Deborah G$0935846
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910459599603321
996   $aError and the growth of experimental knowledge$92116419
997   $aUNINA