A01=Richard Royall
Author_Richard Royall
Bayesian statistics
BCG Scar
Category=PBT
Common Odds Ratio
Conditional Likelihood
Conditional Likelihood Function
Conditional Probability Distribution
Confidence Interval Methodology
Confidence Interval Procedure
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Estimated Likelihood Function
Evidential Interpretation
frequentist inference
function
Independent Binomial Random Variables
Independent Binomial Samples
law of likelihood in scientific research
likelihood
Likelihood Function
Likelihood Interval
Likelihood Ratio
Marginal Likelihood Function
Misleading Evidence
neyman
Neyman Pearson framework
Neyman Pearson Test
Neyman Pearson Theory
Nuisance Parameter
objective statistical measurement
pearson
Prior Probability Distribution
probability theory methods
Profile Likelihood
Profile Likelihood Function
random
ratio
Rejection Trials
Royall Richard
sample
scientific data interpretation
space
theorists
Urn Scheme
variable
Vice Versa
Product details
- ISBN 9780412044113
- Weight: 440g
- Dimensions: 156 x 234mm
- Publication Date: 01 Jun 1997
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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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.
