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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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""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)"

"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)"


"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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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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Amazon.com: 3.5 von 5 Sternen 23 Rezensionen
6 von 6 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen Thorough but not very satisfying 15. September 2014
Von J. Nguyen - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
This is a thoroughly-written book. On one hand I was very glad that it didn't turn out to be a pop-economics type of book. On the other hand, I think the book is largely meant for a more academic audience, so I found it very dense to get through. It describes the problem very clearly, and gives a detailed account of the history behind the statistics, which was interesting.

What I wish the book had, however, was more help for people who want a way out! It spends 90% of time talking about the problem of statistical significance and the history behind it, but I was already in agreement with them so I didn't need any convincing.

I was hoping for more guidance on alternative approaches, or at least more detail on Gosset's thinking and ideas. They make vague references to loss functions, power analysis, etc. as much better approaches, but if you don't know very much about those things you're pretty much on your own to read something else.
114 von 116 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Important work on misuse of statistics by academics 30. Mai 2008
Von David J. Aldous - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe Verifizierter Kauf
Tests of statistical significance are a particular tool which is appropriate in particular situations, basically to prevent you from jumping to conclusions based on too little data. Because this topic lends itself to definite rules which can be mechanically implemented, it has been prominently featured in introductory statistics courses and textbooks for 80 years. But according to the principle "if all you have is a hammer, then everything starts to look like a nail", it has become a ritual requirement for academic papers in fields such as economics, psychology and medicine to include tests of significance. As the book argues at length, this is a misplaced focus; instead of asking "can we be sure beyond reasonable doubt that the size of a certain effect is not zero" one should think about "how can we estimate the size of the effect and its real world significance". A nice touch is the authors' use of the word oomph for "size of effect".

Misplaced emphasis on tests of significance is indeed arguably one of the greatest "wrong turns" in twentieth century science. This point is widely accepted amongst academics who use statistics, but perversely the innate conservatism of authors and academic journals causes them to continue a bad tradition. All this makes a great topic for a book, which in the hands of an inspired author like Steven Jay Gould might have become highly influential. The book under review is perfectly correct in its central logical points, and I hope it does succeed in having influence, but to my taste it's handicapped by several stylistic features.

(1) The overall combative style rapidly becomes grating.

(2) A little history -- how did this state of affairs arise? -- is reasonable, but this book has too much, with a curious emphasis on the personalities of the individuals involved, which is just distracting in a book about errors in statistical logic.

(3) The authors don't seem to have thought carefully about their target audience. For a nonspecialist audience, a lighter How to Lie With Statistics style would surely work better. For an academic audience, a more focused [logical point/example of misuse/what authors should have done] format would surely be more effective.

(4) Their analysis of the number of papers making logical errors (e.g. confusing statistical significance with real-world importance) is wonderfully convincing that this problem hasn't yet gone away. But on the point "is this just an academic game being played badly, or does it have harmful real world consequences" they assert the latter but merely give scattered examples, which are not completely convincing. If people fudge data in the traditional paradigm then surely they would fudge data in any alternate paradigm; if one researcher concludes an important real effect is "statistically insignificant" just because they didn't collect enough data, then won't another researcher be able to collect more data and thereby get the credit for proving it important? Ironically, they demonstrate the harmful real world effect is of the cult is non-zero but not how large it is ......
3 von 3 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen An important criticism, but the book is unnecessarily belabored and often pretentious 3. August 2014
Von kyle peyton - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
I like the authors and generally feel McCloskey's criticisms of the economics profession are both accurate and humorous. I think this book is essentially right about the abuse of statistics in economics and the social sciences more generally but the point is belabored and the delivery is, very often, unnecessarily pretentious. I do think the application of statistics in the social sciences has vastly improved since the publication of this book, whether this book had anything to do with it is a mystery.
5 von 5 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Rare questioning of statistical practices 10. Januar 2013
Von Anthony Nicholls - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
This is an important book with an important message: worry about the size of an effect, not (just) it's statistical significance. Once explained, the idea comes across as very obvious but one that has been missed by whole fields. I wish more would read this book and consider its message before invoking statistics to make major decisions. Certainly something that would have saved a major drug company with which I am familiar. This book will only become more important as data mining and machine learning become more accessible and more interwoven in our lives. Be forewarned and forearmed!
19 von 20 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Interesting thesis but unbearable writing style 26. April 2011
Von Syd Allan - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
Every paragraph in this book is filled with simmering outrage, and every point is made at least twenty times. The main text is 250 pages long; 25 pages would have been much better.

The thesis is interesting (and I suppose it might even be important and valuable). But the writing style is so unbearable that I cannot give this book more than 2 stars.
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