Statistical Evidence

A Likelihood Paradigm

By: Richard Royall

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Hardcover | 1 June 1997 | Edition Number 1

At a Glance

Hardcover
210 Pages

Dimensions(cm)
24 x 16 x 2.5

Hardcover

RRP $357.00

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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.

Industry Reviews

..."provides the explicit concept of evidence missing from the other approaches." -Aslib Book Guide "the book is well written and readable." --Hoben Thomas, Journal of Mathematical Psychology "This (hardback) book provides a very readable discussion of a possible alternative to both the Neyman-Pearson and the Fisherian approaches to the problem of interpreting data as evidencepresent this area of work in a accessible manner with a clear readable style. The main ideas are made easy to understand and well illustrated with some interesting examples, including in an appendix the paradox of the ravens. Diagrams and tables are well used in this respect and the number of formulae is kept low, which aids readabilityprovides a well-presented discussion of an interesting new way of looking at data which would be accessible to most with some understanding of statistics. For this reason I would recommend it to a library." --Thomas Chadwick, University of Newcastle, Biometrics

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