Truth or truthiness : distinguishing fact from fiction by learning to think like a data scientist /

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Bibliographic Details
Author / Creator:Wainer, Howard, author.
Imprint:New York : Cambridge University Press, 2016.
©2016
Description:1 online resource (xviii, 210 page)
Language:English
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12869666
Hidden Bibliographic Details
ISBN:9781316492079
1316492079
9781316424315
1316424316
9781316491195
1316491196
9781107130579
1107130573
Notes:Includes bibliographical references and index.
Print version record.
Summary:"Teacher tenure is a problem. Teacher tenure is a solution. Fracking is safe. Fracking causes earthquakes. Our kids are over-tested. Our kids are not tested enough. We read claims like these in the newspaper, often with no justification other than "it feels right." How can we figure out what is right? Escaping from the clutches of truthiness begins with one question: "What's the evidence?" With his usual verve, and disdain for pious nonsense, Howard Wainer offers a refreshing fact-based view of complex problems in altitude of fields, with special emphasis showing in education how to evaluate the evidence, or lack thereof, supporting various kinds of claims. His primary tool is casual inference: how can we convincingly demonstrate the cause of an effect? This wise book is a must-read for anyone who's ever wanted to challenge the pronouncements of authority figures and a captivating narrative that entertains and educates at the same time. Howard Wainer is a Distinguished Research Scientist at the National Board of Medical Examiners. He has published more than 400 articles and chapters in scholarly journals and books. His book Defeating Deception: Escaping the Shackles of Truthiness by Learning to Think like a Data Scientist, will be published by Cambridge University Press in 2016"--
Other form:Erscheint auch als: Druck-Ausgabe Wainer, Howard. Truth or Truthiness . Distinguishing Fact from Fiction by Learning to Think Like a Data Scientist