The Significance Test Controversy Revisited (eBook)

This book explains the misuses and abuses of Null Hypothesis Significance Tests, which are reconsidered in light of Jeffreys' Bayesian concept of the role of statistical inference, in experimental investigations. Minimizing the technical aspects, the studies focuses mainly on methodological contributions.

The first part of the book gives an overview of the major approaches to statistical testing and an enlightening discussion of the philosophies of Fisher, Neyman-Pearson and Jeffrey. The conceptual and methodological implications of current practices of reporting effect sizes and confidence intervals are also examined and challenged. This sheds new light on the 'significance testing controversy' and provides an appropriate Bayesian framework for a comprehensive approach to the analysis and interpretation of experimental data.

The second part of the book provides concrete Bayesian routine procedures that bypass common misuses of significance testing and are readily applicable in a wide range of real applications. This approach addresses the need for objective reporting of experimental data, that is acceptable to the scientific community. This is emphasized by the name fiducial (from the Latin fiducia = confidence). The fiducial Bayesian procedures provide the reader with a real opportunity to think sensibly about problems of statistical inference.

This book prepares students and researchers to critically read statistical analyses reported in the literature and equips them with an appropriate alternative to the use of significance testing.

 

Bruno Lecoutre  was director of research at the Centre National de la Recherche Scientifique (CNRS) in Paris and then in Rouen since 1996. He was a consultant in the pharmaceutical industry for 20 years. He retired in 2015. He obtained a PhD in experimental psychology from the University of Paris VIII in 1976 and a PhD in mathematics from University René Descartes in Paris in 1980. His main research interests have been in the foundations of statistics and in the development and applications of Bayesian procedures for the analysis of experimental data, including their methodological and computational aspects.