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The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (Economics, Cognition, and Society)

The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (Economics, Cognition, and Society)
Authors: Deirdre Nansen Mccloskey, Steve Ziliak
Publisher: University of Michigan Press
Category: Book

List Price: $24.95
Buy New: $16.47
You Save: $8.48 (34%)



Avg. Customer Rating: 3.5 out of 5 stars 2 reviews
Sales Rank: 20032

Media: Paperback
Number Of Items: 1
Pages: 352
Shipping Weight (lbs): 1
Dimensions (in): 9 x 5.9 x 1

ISBN: 0472050079
Dewey Decimal Number: 330.015195
EAN: 9780472050079
ASIN: 0472050079

Publication Date: February 19, 2008
Shipping: Eligible for Super Saver Shipping
Availability: Usually ships in 2 to 5 weeks

Editorial Reviews:

Product Description

“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).




Customer Reviews:

3 out of 5 stars Exposes the problem, but not much help in solving it   May 14, 2008
 1 out of 1 found this review helpful

The authors do an admirable job of exposing an important issue, but this work only identifies the problem for you, and offers no solution. It seems to go on too long and eventually become s platform for the authors to grip about the injustices that have been served on them in their career. As we have all been indoctrinated into the "cult of significance" through the education system, it would have been nice for the authors to show us how we could do better. Many times we are asked to work statistics on numbers from disciplines on which we have very little knowledge and experience, therefore all we can offer is statistical significance - not material significance, and hope that the people we are working with understand the difference -Most do not and are not prepared to bridge the gap. If there are alternative techniques and methods I am none the wiser, maybe that's my problem.


4 out of 5 stars Bring back effect sizes   March 14, 2008
 17 out of 18 found this review helpful

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.


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