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The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (Economics, Cognition, And Society) Kindle Edition
“McCloskey and Ziliak have been pushing this very elementary, very correct, very important argument through several articles over several years and for reasons I cannot fathom it is still resisted. If it takes a book to get it across, I hope this book will do it. It ought to.”
—Thomas Schelling, Distinguished University Professor, School of Public Policy, University of Maryland, and 2005 Nobel Prize Laureate in Economics
“With humor, insight, piercing logic and a nod to history, Ziliak and McCloskey show how economists—and other scientists—suffer from a mass delusion about statistical analysis. The quest for statistical significance that pervades science today is a deeply flawed substitute for thoughtful analysis. . . . Yet few participants in the scientific bureaucracy have been willing to admit what Ziliak and McCloskey make clear: the emperor has no clothes.”
—Kenneth Rothman, Professor of Epidemiology, Boston University School of Health
The Cult of Statistical Significance shows, field by field, how “statistical significance,” a technique that dominates many sciences, has been a huge mistake. The authors find that researchers in a broad spectrum of fields, from agronomy to zoology, employ “testing” that doesn’t test and “estimating” that doesn’t estimate. The facts will startle the outside reader: how could a group of brilliant scientists wander so far from scientific magnitudes? This study will encourage scientists who want to know how to get the statistical sciences back on track and fulfill their quantitative promise. 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.
Stephen T. Ziliak is the author or editor of many articles and two books. He currently lives in Chicago, where he is Professor of Economics at Roosevelt University. Deirdre N. McCloskey, Distinguished Professor of Economics, History, English, and Communication at the University of Illinois at Chicago, is the author of twenty books and three hundred scholarly articles. She has held Guggenheim and National Humanities Fellowships. She is best known for How to Be Human* Though an Economist (University of Michigan Press, 2000) and her most recent book, The Bourgeois Virtues: Ethics for an Age of Commerce (2006).
- ISBN-13978-0472050079
- PublisherUniversity of Michigan Press
- Publication dateFebruary 11, 2010
- LanguageEnglish
- File size1010 KB
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Editorial Reviews
Review
"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
"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
"McCloskey and Ziliak have been pushing this very elementary, very correct, very important argument through several articles over several years and for reasons I cannot fathom it is still resisted. If it takes a book to get it across, I hope this book will do it. It ought to."
—Thomas Schelling, Distinguished University Professor, School of Public Policy, University of Maryland and 2005 Nobel Prize Laureate in Economics
"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
"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. Smith, Journal of Economic Issues
"[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
"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 authors convincingly argue that environmental quality, jobs, and even lives are at stake."
—M. H. Maier, Glendale Community College, Choice
"With humor, insight, piercing logic and a nod to history, Ziliak and McCloskey show how economists—and other scientists—suffer from a mass delusion about statistical analysis. The quest for statistical significance that pervades science today is a deeply flawed substitute for thoughtful analysis. This hollow pursuit, kept alive by mechanical, conformist thinking, has led to grave and obvious errors. Yet few participants in the scientific bureaucracy have been willing to admit what Ziliak and McCloskey make clear: the emperor has no clothes."
—Kenneth Rothman, Professor of Epidemiology, Boston University School of Health
"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
"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
About the Author
Stephen T. Ziliak is the author or editor of many articles and two books. He currently lives in Chicago, where he is Professor of Economics at Roosevelt University.
Deirdre N. McCloskey, Distinguished Professor of Economics, History, English, and Communication at the University of Illinois at Chicago, is the author of twenty books and three hundred scholarly articles. She has held Guggenheim and National Humanities Fellowships. She is best known for How to Be Human* Though an Economist (University of Michigan Press, 2000), and her most recent book, The Bourgeois Virtues: Ethics for an Age of Commerce (2006).
Product details
- ASIN : B07PH91LCL
- Publisher : University of Michigan Press (February 11, 2010)
- Publication date : February 11, 2010
- Language : English
- File size : 1010 KB
- Text-to-Speech : Enabled
- Screen Reader : Supported
- Enhanced typesetting : Enabled
- X-Ray : Not Enabled
- Word Wise : Enabled
- Sticky notes : On Kindle Scribe
- Print length : 341 pages
- Page numbers source ISBN : 0472050079
- Best Sellers Rank: #903,977 in Kindle Store (See Top 100 in Kindle Store)
- #241 in Econometrics & Statistics
- #406 in Economic Theory (Kindle Store)
- #663 in Economic History (Kindle Store)
- Customer Reviews:
About the authors
Discover more of the author’s books, see similar authors, read author blogs and more
Discover more of the author’s books, see similar authors, read author blogs and more
Stephen T. Ziliak is Professor of Economics at Roosevelt University, Chicago, where as a faculty member he also served on the Board of Trustees (from 2010 to 2013). His previous appointments include Emory University and the Georgia Institute of Technology, where he was voted Faculty Member of the Year (in 2002) and Most Intellectual Professor (in 2003). At the University of Iowa he earned (in 1996) the Ph.D. in Economics and, at the same time, the Ph.D. Certificate in the Rhetoric of the Human Sciences. He has been a Visiting Professor of Economics, Statistics, Rhetoric, Justice, Social Welfare, and Methodology at leading universities of the United States, Belgium, Denmark, England, France, Turkey, and the Netherlands.
Ziliak's early work was on econometric methodology and quantitative economic history, where he established himself as a leading economic historian of welfare and charity in the United States. He, at the same time, published a seminal article of econometrics, "The Standard Error of Regressions" (1996, with Deirdre N. McCoskey), prior to tackling the history, philosophy, and practice of all the testing sciences.
His pioneering contributions to the seemingly disparate fields of economic statistics and poetry include Guinnessometrics, the cult of statistical significance, haiku economics, and renganomics. His research has appeared in many leading journals, such as The Lancet, Journal of Economic Literature, Journal of Economic Perspectives, Poetry, Biological Theory, International Journal of Forecasting, Journal of Economic History, and Journal of Wine Economics.
He is the lead author of The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (2008) a best-selling and critically-acclaimed book at the University of Michigan Press, with Deirdre N. McCloskey; with McCloskey and Arjo Klamer he is co-author of The Economic Conversation, an evolving textbook and blog, emphasizing dialogue and openness; and he edited and contributed to Measurement and Meaning in Economics: The Essential Deirdre McCloskey (Edward Elgar, 2001). An Associate Editor of Historical Statistics of the United States (Cambridge, 2006), Ziliak's work has been featured in Science, Nature, The Economist, Poetry, Wall Street Journal, BBC, NPR, Chronicle of Higher Education, Slate, Salon, Washington Post, Financial Times, New York Times, and the Supreme Court of the United States.
Learn more at http://sites.roosevelt.edu/sziliak and http://stephentziliak.com
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The authors of the book did I fine job of fleshing out the logical errors in the system. They pointed out an additional error that I hadn't found yet.
This is an excellent book that raises some very profound questions about scientific practice in modern academia. It should be read by graduate students in any discipline that fancies itself to be "scientific."
Perhaps the most fascinating aspect of the book is that it was well written and entertaining as well as informative, even though it was written by two economists! That factoid makes the book significant by itself!!!!!!
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 ......
My cholesterol numbers were bad, and though I told the doc I wouldn't take a statin, he looked at the chart and clucked, "with LDL this high you're at risk for pancreatitis. You'd better get those numbers down." And he wrote a prescription.
I filled it, and as usual I got the dozen pages of onionskin paper with the pharmacological details. I decided to read them, and I was flabbergasted. Translated into English, this is what it said:
We have done a Big Study, oh yes we have, and we have Numbers: look at them!
And we have analyzed those numbers and we have Conclusions. And we used
Statistics, so you know we must be right.
The first thing you should know is that people who took our drug died at a higher
rate than people who didn't. 11% higher approximately. And if this were a court,
we'd have to say that the drug is guilty on the preponderance of the evidence,
because we figure the odds are about 4 to 1 that the drug did it. But our Statistics
tells us to ignore anything that is not beyond reasonable doubt, and 4-to-1 doesn't
make it, so we think you should ignore the fact that the drug does more harm than good.
What you should focus on is that it lowers your LDL. We showed that beyond reasonable
doubt - the odds are at least 22 to 1. And low LDL is good. So our advice is, take it.
That is what it said.
The question for us all is, How did we come to this? How can the scientific hierarchy, from the FDA down to kindly Doctor Brown, think that it is anything but crazy to take this drug? Am I looking to have "his LDL was low" on my tombstone? Is there no judgement that would say that costing lives is very bad, and lowering LDL is of no value of itself if it doesn't save lives?
The message of the book is that things are every bit as bad as you might fear.
The authors show how the pursuit of science has been shunted off into a search for "statistical significance" which has nothing whatever to do with scientific significance or importance. They give a pretty good explanation for how things got so messed up.
This book is of the highest possible importance for anyone who uses or teaches statistical inference. But anyone who knows a little statistics should definitely read the book, and anyone who knows a little math might enjoy it.
For it is engagingly written. Think of it, a book on statistical significance! Could anything be more dull? But the book has the pace of a potboiler. There are witty jokes, haikus, appeals to outrage and to laughter. There is a hero (William Gosset, aka Student) and a villain (the evil Sir Ronald Fisher). There are fables and parables.
Most importantly, there is the truth: that science based solely on rejecting the null hypothesis is sterile, unconvincing even to its practitioners, and extremely costly, both in money and in lost time and lives. You will weep when you see how thoroughly the sciences that use statistics (such economics, psychology, sociology, medicine) have come under the grip of a Statistical-Academic Complex that persists in significance tests because significance tests get you promoted, never mind the real scientific value.
The problem is real. Mistakes are being made daily because of sloppy statistics. And remember that drug that the doc wanted me to take to ward off pancreatitis? In the fine print the study showed that the drug didn't help people with pancreatitis either. Ba-dum.