The Cult of Statistical Significance: How the Standard Error Costs US Jobs, Justice, And Lives. The Cult of Statistical Significance: How the Standard Error Costs US Jobs, Justice, And Lives.

The Cult of Statistical Significance: How the Standard Error Costs US Jobs, Justice, And Lives‪.‬

The Cato Journal 2008, Spring-Summer, 28, 2

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Publisher Description

The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives Stephen T. Ziliak and Deirdre N. McCloskey Ann Arbor, Mich.: University of Michigan Press, 2008, 3,52 pp. How do many scientific disciplines estimate and report results? Practitioners estimate regression models or conduct difference-of-means tests through experiments. And they report which results are significant and which are not (i.e., different from zero with 95 percent confidence). In this important book, Ziliak and McCloskey have three objectives: to remind us that such research may be mindless, unscientific, and costly; to explicate the intellectual history of significance testing and the straggles among those professors who developed sampling and statistical testing; and to illustrate the correct way to conduct research and praise those few who report their research properly.

GENRE
Politics & Current Events
RELEASED
2008
March 22
LANGUAGE
EN
English
LENGTH
8
Pages
PUBLISHER
Cato Institute
SELLER
The Gale Group, Inc., a Delaware corporation and an affiliate of Cengage Learning, Inc.
SIZE
242.6
KB

Customer Reviews

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Engineer's Review

I am graduating as an Industrial Engineer from Northern Illinois University this weekend. I cannot speak for other fields, but we have been taught to look beyond the significance level and focus on reporting p values. Any one who has taken a college level stats course, did well, and understands that rejecting a null hypothesis should be done because the difference between it and the alternative was due to randomness will not gain much from reading this. It is however written well and could help those who preform significance tests without good knowledge of stats.

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