000			01973nam a22002657a 4500
999			_c1839 _d1839
003			OSt
005			20191014104936.0
008			191014b ||||| |||| 00| 0 eng d
020			_a978-1-10-766464-7
040			_cIZA
100			_aMayo, Deborah G. _94845
245			_aStatistical Inference as Severe Testing: How to Get Beyond the Statistics Wars
260			_aCambridge, _bCambridge University Press, _c2018
300			_a486 pages
520			_aMounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.
653			_ascience
653			_aprobabilty
653			_astatistics
653			_astatistical methods
653			_astatistical inference
653			_aphilosophy
653			_aphilosophy of statistics
856			_uhttps://www.cambridge.org/core/books/statistical-inference-as-severe-testing/D9DF409EF568090F3F60407FF2B973B2#fndtn-information _yPublisher's website
942			_2ddc _cBO