PDF Ebook The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (Economics, Cognition, And Society)
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The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (Economics, Cognition, And Society)
PDF Ebook The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (Economics, Cognition, And Society)
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Product details
Series: Economics, Cognition, And Society
Paperback: 348 pages
Publisher: University of Michigan Press; 1st edition (February 19, 2008)
Language: English
ISBN-10: 0472050079
ISBN-13: 978-0472050079
Product Dimensions:
6 x 0.9 x 9 inches
Shipping Weight: 15.2 ounces (View shipping rates and policies)
Average Customer Review:
3.7 out of 5 stars
24 customer reviews
Amazon Best Sellers Rank:
#565,840 in Books (See Top 100 in Books)
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.
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 ......
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.
My professor was one of the authors who wrote this book. He taught me Economics
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.
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!
I sympathize with reviewers turned-off by the tone and style of argument in the book. Yet it might be argued that the in-your-face tone and character dramatization are not "beneath the authors" but rather called for by the lowly fashion of science so-far tested.The (strikingly refined and professionally mature) reviewership correctly cite that more emotionally-neutral coverage of the same topics exist. Yet that work has done little to penetrate the dominant norm: third-year, bachelor-degree, applied-intermediate-course significance testing.And anyway, McCloskey's point about oomph goes long beyond the mathematical derivation of the size of a Beta. It is a point that *must* be intuitively argued and broadly applied, less professionals at all levels of research continue to bicker about miniscule empirical effects and their directions when huge residuals or theoretically robust variables stand screaming, elephants in the room.All of that said, yes I would have liked a clearer theoretical treatment in the beginning chapters.
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