Chapman and Hall/CRC
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.
"…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 evidence…present 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 readability…provides 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
The First Principle
The Law of Likelihood
Relativity of Evidence
Strength of Evidence
Testing Simple Hypotheses
Irrelevance of the Sample Space
The Likelihood Principle
Evidence and Uncertainty
Neyman-Pearson Statistical Theory
Evidential Interpretation of Results of Neyman-Pearson Decision Procedures
Neyman-Pearson Hypothesis Testing in Planning Experiments: Choosing the Sample Size
A Method for Measuring Statistical Evidence: The Test of Significance
The Rationale for Significance Tests
Troubles with p-Values
A Sample of Interpretations
The Illogic of Rejection Trials
Confidence Sets from Rejection Trials
Alternative Hypothesis in Science
Paradigms for Statistics
An Alternative Paradigm
Probabilities of Weak and Misleading Evidence: Normal Distribution Mean
Understanding the Likelihood Paradigm
Evidence about a Probability: Planning a Clinical Trial and Interpreting the Results
Resolving the Old Paradoxes
Why is Power of Only 0.80 OK?
Peeking at Data Repeated Tests
Testing More than One Hypothesis
What's Wrong with One-SIded Tests?
Must the Significance Level be Predetermined?
And is the Strength of Evidence Limited by the Researcher's Expectations?
Looking at Likelihoods
Evidence about Hazard Rates in Two Factories
Evidence about an Odds Ration
A Standardized Mortality Rate
Evidence about a Finite Population Total
Determinants of Plans to Attend College
Evidence about the Probabilities in a 2x2x2x2 Table
Evidence from a Community Intervention Study of Hypertension
Effects of Sugars on Growth of Pea Sections: Analysis of Variance
Synthetic Conditional Likelihoods
Bayesian Statistical Inference
Bayesian Statistical Models
Subjectivity in Bayesian Models
The Trouble with Bayesian Statistics
Are Likelihood Methods Bayesian?
Objective Bayesian Inference
Bayesian Integrated Likelihoods
Appendix: The Paradox of the Ravens