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The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (Economics, Cognition, and Society)
by Deirdre Nansen McCloskey, Steve Ziliak
from University of Michigan Press
Customer Reviews:
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Avg. Customer Rating: 3.5 / 5.0 
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Oh so needed! 
The message that I took from this book was two fold:
1) Queries into existence (does a relationship exist?) are metaphysical, not scientific.
2) Looking to the statistical significance of a relationship must be coupled with a look at the magnitude of the effect/correlation and the "big picture" of the problem being investigated.
The authors set the scene well, write lucidly, and provide illuminating examples. I would think this book would be "must-read" for anyone preparing to engage the... more info
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Mean-spirited and Misguided 
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... more info
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Important work on misuse of statistics by academics 
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... more info
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Good but could have been better
This book provides strong arguments that scientists often use tests of statistical significance as a ritual that substitutes for thought about how hypotheses should be tested.
Some of the practices they criticize are clearly foolish, such as treating data which fall slightly short of providing statistically significant evidence for a hypothesis as reason for concluding the hypothesis is false. But for other practices they attack, it's unclear whether we can expect scientists to be reasonable enough to... more info
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