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The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (Economics, Cognition, and Society) (Englisch) Gebundene Ausgabe – 19. Februar 2008

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Produktinformation

Produktbeschreibungen

Pressestimmen

"Despite appearing to be a book of limited appeal - it is after all a book that looks at a set of statistical techniques - it is one that has immense social implications. We live in an age where ideologies have largely been cast aside and instead we are governed increasingly by a class of politicians and civil servants who aim for 'evidence-based' policy-making. When that evidence is based on statistically significant results that ignore any quantification of results then we all have reason to pay attention."
"London Book Review"
--NA"London Book Review" (12/23/2008)"

"Persuading professionals that their procedures are wrong is a long and lonely task. McCloskey, joined later by Ziliak, has been conducting such a crusade against the misuse of significance testing for over 25 years. This book presents their argument, gives lots of examples of the adverse consequences of misuse, and provides some history of the controversy, which dates from the origins of mathematical statistics."
Ron P./i>
--Ron P. Smith"Journal of Economic Issues" (01/01/2009)"

""The Cult of Statistical Significance" has virtues that extend beyond its core message. It is clearly written and should be accessible to those who have neither formal training in statistics nor a desire to secure any. It is full of examples that illustrate why it is the strength of relationships and not their statistical significance that mainly matters."
Richard Lempert, "Law and Social Inquiry"
--Richard Lempert"Law and Social Inquiry" (01/01/2009)"

"A clear trade-off: how much confidence [in a result] is "enough" depends on the costs of further research and the benefits of extra precision. Ziliak and his co-author Deirdre McCloskey argue in "The Cult of Statistical Significance" that most academic disciplines have forgotten this trade-off . . . A sharp line for statistical significance makes no sense, and it has a cost."
Tim Harford, "The Financial Times"
--Tim Harford"Financial Times" (02/07/2009)"

"If not Fisherian significance, what should be the Holy Grail of statistics? Ziliak and McCloskey . . . answer: "Oomph." We should identify quantities that matter and measure them, not merely determine whether they can be distinguished from the null (meaning no effect) at some predetermined likelihood level. The validity of this point I take to be virtually self-evident. Yet statistical tests that ignore quantity remain pervasive, as the authors demonstrate through quantitative analyses of the contents of some very prestigious journals of economics, psychology, and medicine."
Theodore Porter, "Science"
--Theodore Porter"Science" (06/05/2009)"

"The book is a model of scholarship, transparent in its method, wide-reaching in its disciplinary expertise, and highly literate, including occasional haiku poems and humor such as, 'If the variable doesn't fit/you may not have to acquit.' The authorsconvincingly argue that environmental quality, jobs, and even lives are at stake."
M. H./i>
--M. H. Maier"Choice" (10/21/2009)"

"What is important is a shift of emphasis away from a dichotomous world of true and false towards a recognition of "oomph." This is what the presented book tries to achieve. It is also fun to read, rich with historical information and an excellent reminder of what empirical work of any sort is all about."
Walter Kramer, "Stat Papers"
--W. Kramer "Stat Papers ""

"[Steve Ziliak and Deirdre McCloskey] explain to us why the misunderstanding of statistical significance has lead to bad government policy making and how one particularly famous brewery employed the technique to improve the pints we enjoy today."
Tim Harford, BBC
--Tim Harford"BBC" (01/23/2009)"

Synopsis

"Statistical significance," a technique that dominates medicine, economics, psychology, and many other scientific fields, has been a huge mistake. The outcome is a case study in bad science - how it originates and how it grows. These sciences, from agronomy to zoology, the authors find, engage "testing" that doesn't test and "estimating" that doesn't estimate. Heedless of magnitude and of a genuine engagement with alternative hypotheses, they "testimate." "Null hypothesis significance testing" is in other words a scientific train-wreck, about which a small group of statisticians have been warning for a century.Ziliak and McCloskey's book shows field by field how the wreck happened, reports on the fatalities, and offers a quantitative way forward. The facts will startle the outside reader: how could a group of brilliant scientists wander so far away from scientific magnitudes? And it will inspirit the scientists who seek conscious interpretations of "oomph" rather than arbitrary columns of t-tests: how can the statistical sciences get back on track, and fulfill their quantitative promise?Ziliak and McCloskey measure the disaster in their home field of economics, and in psychology, epidemiology, and medical science.

They touch as well on law, biology, psychiatry, pharmacology, sociology, political science, education, forensics, and other fields in the grip of "significance." The book shows for the first time how wide the disaster is, and how bad for science, and it traces the problem to its historical, sociological, and philosophical roots. Many statisticians have complained about it before, but have complained science-by-science.

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Format: Taschenbuch Verifizierter Kauf
I was very pleased with this book. It has a nice philosophical and transdisciplinary approach to statistics with a light emphasis on economics. Sometimes the authors appear a bit too self righteous for my taste, but its nevertheless worth reading and very interesting. I definitly got some new ideas!
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Format: Taschenbuch Verifizierter Kauf
This book is useful for the casual reader of medical, psychological and economic research reports. It is well written in a lively language, and it is interesting from a history of science point of view; in practical terms, however, it is a but lenghty: Any reader will readily understand the main issue: statistical significance by itself is insufficient for decision making without information on the measured or expected effect size.
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