000			01835 a2200253 4500
003			OSt
005			20220228165148.0
008			220223b xxu||||| |||| 00| 0 eng d
020			_a9781107664647
040			_cIIT Kanpur
041			_aeng
082			_a519.54 _bM454s
100			_aMayo, Deborah G.
245			_aStatistical inference as severe testing _bhow to get beyond the statistics wars _cDeborah G. Mayo
260			_bCambridge University Press _c2018 _aCambridge
300			_axvi, 486p
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.
650			_aDeviation (Mathematics)
650			_aMathematical statistics
650			_aError analysis (Mathematics)
650			_aFallacies (Logic)
650			_aInference
942			_cBK
999			_c565079 _d565079