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Truth or Truthiness: Distinguishing Fact from Fiction by Learning to Think Like a Data Scientist

Contributor(s): Wainer, Howard (Author)

ISBN: 9781107130579

Publisher: Cambridge University Press

Hardcover
$37.00
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Pub Date: December 1, 2015

Dewey: 001.42

LCCN: 2015040736

Lexile Code: 0000

Features: Bibliography, Illustrated, Index, Maps, Price on Product

Target Age Group: NA to NA

Physical Info: 0.72" H x 9.35" L x 6.13" W ( 1.01 lbs) 232 pages

Descriptions, Reviews, etc.

Description: 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 every day, 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 simple question: 'what is the evidence?' With his usual verve and flair, Howard Wainer shows how the sceptical mindset of a data scientist can expose truthiness, nonsense, and outright deception. Using the tools of causal inference he evaluates the evidence, or lack thereof, supporting claims in many fields, with special emphasis in education. This wise book is a must-read for anyone who has ever wanted to challenge the pronouncements of authority figures and a lucid and captivating narrative that entertains and educates at the same time.

Brief description: Howard Wainer is a Distinguished Research Scientist at the National Board of Medical Examiners. He has published more than four hundred articles and chapters in scholarly journals and books.

Review Quotes: 'Such a book is desperately needed, given the prevalence of 'truthiness' - a term coined by the comedian Stephen Colbert to mean things that are 'felt to be true ... without regard to evidence, logic, intellectual examination or facts' ... To paraphrase one of the quotes the author likes to make frequent use of, what you get here is not simply tricks of the trade but the trade itself. Wainer has deep experience of applying statistical thinking to societal questions and he is the kind of master statistical craftsman and communicator at whose feet we all wish we could learn.' Paul Craze, Significance

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