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Entropy 2016, 18(5), 199; doi:10.3390/e18050199

Beyond Hypothesis Testing

Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Academic Editors: Julio Stern and Adriano Polpo
Received: 6 April 2016 / Revised: 13 May 2016 / Accepted: 17 May 2016 / Published: 20 May 2016
(This article belongs to the Special Issue Statistical Significance and the Logic of Hypothesis Testing)
View Full-Text   |   Download PDF [188 KB, uploaded 20 May 2016]


The extraordinary success of physicists to find simple laws that explain many phenomena is beguiling. With the exception of quantum mechanics, it suggests a deterministic world in which theories are right or wrong, and the world is simple. However, attempts to apply such thinking to other phenomena have not been so successful. Individually and collectively we face many situations dominated by uncertainty, about weather and climate, about how wisely to raise children, and how the economy should be managed. The controversy about hypothesis testing is dominated by the tension between simple explanations and the complexity of the world we live in. View Full-Text
Keywords: significance tests; confidence intervals; Bayesian analysis; hypothesis tests; prior distributions; posterior distributions significance tests; confidence intervals; Bayesian analysis; hypothesis tests; prior distributions; posterior distributions
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kadane, J.B. Beyond Hypothesis Testing. Entropy 2016, 18, 199.

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