"Statistical significance is neither necessary nor sufficient for proving a commercial or scientific result." When two University of Michigan Press authors, Deirdre N. McCloskey and Stephen T. Ziliak, first began saying so, back in the 1980s, most of their colleagues dismissed their logic and findings. Ziliak himself, a Trustee and Professor of Economics at Roosevelt University, was advised by some of his professors to stop talking about statistical significance. But he and McCloskey, co-authors of the critically acclaimed book, The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (2008), kept talking and talking.
Now their view is the rule of law. On March 22nd, 2011 the Supreme Court of the United States decided the case of Matrixx v. Siracusano, arguing unanimously that a bright-line standard of statistical significance is neither necessary nor sufficient for proving an adverse effects in medical, drug, and other industries reporting to the Securities and Exchange Commission.
The distinguished economists had a more immediate role in the case. On November 12, 2010, they were invited to file an amicus brief with the Supreme Court of the United States. During the January 11th, 2011 oral argument, Justice Sotomayor, who wrote the unanimous opinion, thanked "amici" (that is our UMP authors) for doing a "wonderful" job explaining the difference between statistical significance and practical importance. And several of the other briefs - including one filed by the United States of America - are influenced by their work.
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.