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The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (Economics, Cognition, and Society) (Englisch) Taschenbuch – 15. April 2008


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The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (Economics, Cognition, and Society) + Bourgeois Dignity: Why Economics Can't Explain the Modern World + The Bourgeois Virtues: Ethics for an Age of Commerce
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Produktinformation

  • Taschenbuch: 321 Seiten
  • Verlag: Univ of Michigan Pr (15. April 2008)
  • Sprache: Englisch
  • ISBN-10: 0472050079
  • ISBN-13: 978-0472050079
  • Größe und/oder Gewicht: 22,8 x 15,3 x 2,4 cm
  • Durchschnittliche Kundenbewertung: 5.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)
  • Amazon Bestseller-Rang: Nr. 105.744 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)

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Produktbeschreibungen

Pressestimmen

"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 " -- Dieser Text bezieht sich auf eine andere Ausgabe: Gebundene Ausgabe .

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." This 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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Einleitungssatz
For the past eighty years it appears that some of the sciences have made a mistake by basing decisions on statistical "significance." Lesen Sie die erste Seite
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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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Amazon.com: 23 Rezensionen
99 von 101 Kunden fanden die folgende Rezension hilfreich
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 ......
132 von 157 Kunden fanden die folgende Rezension hilfreich
Mean-spirited and Misguided 30. Juni 2008
Von Peter Kwok - Veröffentlicht auf Amazon.com
Format: Taschenbuch
I attended a seminar by McCloskey when she announced she was working on this then-upcoming book. So I knew beforehand that its style would be more like a victim-tells-all revenge than a fun-seeking discovery typical of most popular science books. The first half of the book (up to Chapter 13) did turn out to be bitter. However, at least that part was largely based on facts, such as a comprehensive count of academic papers failing to meet certain standards. The second half of the book was devoted to the biographies of key persons who led to the rise of what the authors called the "cult of statistical significance". The book lost any pretense of integrity at that point, and just started slinging muds. Gosset was portrayed as a good-natured figure who worked hard like a bee; and Fisher, a mad scientist who stole the labor of others and would attack people by any means to defend his status. At one point the authors didn't even bother to call Fisher by his name, and just referred to him as the Wasp. They also dragged Fisher's mother into the ordeal by making suggestions that she was responsible for turning Fisher into a cold-hearted person that they claimed.

I was not only disgusted by this kind of tabloid sensationalism, but was also disappointed by how little useful information I got out of this long-awaited book. The authors "irrationalized" the popularization of statistical significance by framing it as the work of a cult. To further illegitimatize the use of statistical significance, they argued that it is wrong to rely on it to evaluate scientific hypotheses because (1) what we really want is how likely for a hypothesis to be true given the data, not the other way around; and (2) there are other clues just as, if not more, important, especially the effect size. These could have been reasonable positions if they did not make statistical significance a scapegoat for being a "fallacy" just because it is defined on the likelihoods of observing data given the hypotheses. As the way it is defined, statistical significance provides a measure of precision. That's all. Just because it doesn't answer all the questions of scientific interest doesn't mean it provides no useful information and certainly doesn't automatically make it a fallacy. Furthermore, many hypothesis tests used in academic researches are based on likelihood "ratios" rather than just the conditionals. At least there would be NO fallacy for the believers of the Likelihood Principle. It is quite regrettable that they fail to elaborate on such crucial information to make other people look stupid, whether it was their intention or not. As for the second point, I agree that researchers should have paid more attention to other factors, such as statistical power and sample size, IN ADDITION TO statistical significance. But I think it is misguided to hail any ban on reporting statistical significance as a heroic act of revolt as the authors did in the book. One can report all the effect sizes he wants. But it all means nothing if his inferences are what they appear to be mostly due to "bad luck" in sampling the wrong subjects.

If my views above are on the right track, then this book would serve the research community no good by martyrizing Gosset and demonizing Fisher. There has been no cult all along. If we are justified in believing that some vested interests overemphasized statistical significance to divert our attention away from the more important issues, then we should encourage people (authors and readers alike) to focus on those more important issues instead of treating statistical significance as if it were irrelevant. For a more serious and more informative discussion on this topics, I would recommend Chow's Statistical Significance: Rationale, Validity and Utility (Introducing Statistical Methods) . His first chapter explains the key issues in 12 pages with more varieties of arguments and more intellectually stimulating details than what Ziliak and McClosky attempted in 251 pages.

I give 3 stars for this book's good intent but average quality, and, on top of that, took 1 star off for its mean-spirited rhetorics.
52 von 60 Kunden fanden die folgende Rezension hilfreich
Disappointing 10. Dezember 2008
Von Sergei Soares - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
I know and admire Deirdre McCloskey's work and I am an empirical economist who has to work every day with t and F tests and p-values. So I was quite excited when I read that this particular author had co-authored a book on this particular subject.

Unfortunately, I was quite disappointed. I was expecting either a narrative of errors made in the name of statistical significance or an in-depth analysis of what tests really mean. The authors do neither.

In the first half of the book, they superficially narrate the problems with the Vioxx clinical trials, but tell few other stories of how the standard error "costs jobs, justice and lives." A narrative along the lines of "Normal Accidents", by Charles Perrow, which documents an extensive list of accidents to tell of the perils of complexity, would have made for much better reading. After reading the book, I am none the wiser as to why or how the jobs, justice and lives were lost to statistical significance.

Alternatively, the book could have explained in terms clear to those who do not work every day with tests what is meant by significance and power of a test and what these terms really mean. I have never seen an explanation of these terms that is really clear and sticks in your mind. Unfortunately this was not the case either. The authors mention that statistical significance is more complex than just p-values, affirm that most economists not understand why, and leave it at that. They confuse more than explain.

As a final problem, the book takes a good versus evil attitude that is nowhere good science. Gosset is good and Fischer is bad. Please.

In conclusion, while I agree with the author's main thesis, their book argues it very poorly, very lengthily, and very tediously.
44 von 51 Kunden fanden die folgende Rezension hilfreich
Bring back effect sizes 14. März 2008
Von Coert Visser - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
This book shows how many scientific disciplines rely way too much on the concept of statistical significance. I have read the book and I find it convincing. The authors show how the focus on statistical significance has taken away attention for 'real' significance. In other words: the focus on statistical significance often means that researchers fail to ask whether their findings matter. In statistics, a result is called statistically significant if it is unlikely to have occurred by chance. So testing for statistical significance is asking the question how likely it is that an effect exists. It tries to answer that question by looking at how precisely the effect can be measured. It does not answer at all how strong and important this effect is. And this latter question about the effect size is much more important from a scientific and a practical perspective. Statistical significance does not imply an effect is important, lack of statistical significance does not mean an effect is not important. You may ask: how can an effect be important that is not statistically significant? The answer to your question has to do with HOW a statistical significance test tries to answer the question of whether an effects does or not exist, which is by looking at HOW PRECISELY the (presumed) effect can be measured. There are circumstances in which an effect is important yet can not be measured precisely. This would be the case when there is a lot of variability in the effect. When an effect is strong YET highly variable (for instance ranging between 30 and 70), statistical significance tests say the effect cannot be measured precisely which can lead to the conclusion: not statistically significant. At the same time, a weaker effect with lower variability (for instance ranging between 4 and 5) could be measured more precisely, which might lead to the conclusion 'statistically significant'.
Mind you, the book is NOT a plea against quantitative research nor statistical analysis. On the contrary. It is a plea for doing it and doing it right by bringing back focus on effect sizes in social science.
15 von 15 Kunden fanden die folgende Rezension hilfreich
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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