LEADER 04906nam 22007575 450 001 9910254097103321 005 20250329111055.0 010 $a9783319397566 010 $a3319397567 024 7 $a10.1007/978-3-319-39756-6 035 $a(CKB)3710000000748015 035 $a(DE-He213)978-3-319-39756-6 035 $a(MiAaPQ)EBC6314621 035 $a(MiAaPQ)EBC5579125 035 $a(Au-PeEL)EBL5579125 035 $a(OCoLC)953865751 035 $a(PPN)194517128 035 $a(EXLCZ)993710000000748015 100 $a20160630d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aUncertainty $eThe Soul of Modeling, Probability & Statistics /$fby William Briggs 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XIX, 258 p. 23 illus.) 311 0 $a9783319397559 311 0 $a3319397559 320 $aIncludes bibliographical references and index. 327 $aTruth, Argument, Realism -- Logic -- Induction and Intellection -- What Probability Is -- What Probability Is Not -- Chance and Randomness -- Causality -- Probability Models -- Statistical and Physical Models -- Modelling Goals, Strategies, and Mistakes. 330 $aThis book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance". The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, such as out-of-the-box regression, cannot help in discovering cause. This new way of looking at uncertainty ties together disparate fields ? probability, physics, biology, the ?soft? sciences, computer science ? because each aims at discovering cause (of effects). It broadens the understanding beyond frequentist and Bayesian methods to propose a Third Way of modeling. Presents a complete argument showing why probability should be treated as a part of logic Broadens understanding beyond frequentist and Bayesian methods, proposing a Third Way of modeling Proposes that p-values should die, and along with them, hypothesis testing William M. Briggs, PhD, is Adjunct Professor of Statistics at Cornell University. Having earned both his PhD in Statistics and MSc in Atmospheric Physics from Cornell University, he served as the editor of the American Meteorological Society journal and has published over 60 papers. He studies the philosophy of science, the use and misuses of uncertainty - from truth to modeling. Early in life, he began his career as a cryptologist for the Air Force, then slipped into weather and climate forecasting, and later matured into an epistemologist. Currently, he has a popular, long-running blog on the subjects written about here, with about 70,000 - 90,000 monthly readers. 606 $aStatistics 606 $aProbabilities 606 $aMathematics$xPhilosophy 606 $aStatistics 606 $aKnowledge, Theory of 606 $aLogic 606 $aStatistical Theory and Methods 606 $aProbability Theory 606 $aPhilosophy of Mathematics 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aEpistemology 606 $aLogic 615 0$aStatistics. 615 0$aProbabilities. 615 0$aMathematics$xPhilosophy. 615 0$aStatistics. 615 0$aKnowledge, Theory of. 615 0$aLogic. 615 14$aStatistical Theory and Methods. 615 24$aProbability Theory. 615 24$aPhilosophy of Mathematics. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aEpistemology. 615 24$aLogic. 676 $a511.31 700 $aBriggs$b William$4aut$4http://id.loc.gov/vocabulary/relators/aut$0104342 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254097103321 996 $aUncertainty$91523712 997 $aUNINA