E-raamat: Statistical Evidence: A Likelihood Paradigm [Taylor & Francis e-raamat]

For generations of students schooled in the statistical theories of Fisher and Neyman-Pearson, Royall (biostatistics, Johns Hopkins U.) delivers an alternative likelihood paradigm focusing on what he deems the neglected Law of Likelihood. After weighing their assets and weaknesses, he persuasively dares question what most were taught as gospels of the scientific method: What is wrong with one-sided tests? Why not use the most powerful test? Must the significance level be predetermined? Is the strength of the evidence limited by the researcher's expectations? Includes exercises in this "new" statistics. Annotation c. by Book News, Inc., Portland, Or.

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.