Statistical Evidence: A Likelihood Paradigm

by Richard Royall

Interpreting statistical data as evidence, Statistical Evidence: A Likelihood Paradigm focuses on the law of likelihood, fundamental to solving many of the problems associated with interpreting data in this way. Statistics has long neglected this principle, resulting in a seriously defective methodology. This book redresses the balance, explaining why science has clung to a defective methodology despite its well-known defects. After examining the strengths and weaknesses of the work of Neyman and Pearson and the Fisher paradigm, the author proposes an alternative paradigm which provides, in the law of likelihood, the explicit concept of evidence missing from the other paradigms. At the same time, this new paradigm retains the elements of objective measurement and control of the frequency of misleading results, features which made the old paradigms so important to science. The likelihood paradigm leads to statistical methods that have a compelling rationale and an elegant simplicity, no longer forcing the reader to choose between frequentist and Bayesian statistics.

Genres: Mathematics, Science

191 pages, Kindle Edition

First published June 1, 1997

Reviews

[Aleksandar Stupar]

Really good book, giving insight into basic principles of statistics and the questions it is trying to answer. It illustrates the shortcomings of the established methods and proposes an alternative. Worth a read for anyone who is ever considering doing a significance test.

[Guillaume Belanger]

This book was a revelatory eye-opening experience for me. I now feel that everyone involved in working with data and statistics must read this book.

[Wilte]

F.S.W. M&T 70.03/6424