## Statistical Evidence: A Likelihood ParadigmInterpreting 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. |

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This thoughtful book continues an important tradition of statistical reasoning that challenges the usual machinery of hypothesis tests and p-values. It is well worth reading if you are interested in the foundations of inference, but it would be tough going without a good elementary course in statistics. --DHK

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### Common terms and phrases

7-value alternative Bayesian statistics Bernoulli trials better supported binomial Chapter conditional likelihood confidence interval data as evidence data say ECMO estimator evidence for H2 evidence in favor evidential interpretation example experiment fairly strong evidence favor of H2 Figure find strong evidence Fisherian frequentist given H2 is true H2 over H hypothesis testing implies law of improbability law of likelihood lihood likelihood function likelihood interval likelihood principle likelihood ratio marginal likelihood mean misleading evidence Neyman-Pearson tests Neyman-Pearson theory nuisance parameter null hypothesis observations as evidence odds ratio outcome pA(x paradigm prior probability distribution prob probabilities of weak probability density function probability model probability of misleading problem profile likelihood random variable reject H rejection trials sample space shows significance level significance tests specified standard statistical evidence statistical methods strong evidence supporting success Suppose test procedure tion tosses Type I error value of 9 versus H2 weak evidence

### References to this book

Common Errors in Statistics (and How to Avoid Them) Phillip I. Good,James W. Hardin Limited preview - 2006 |

Early Childhood Educational Research: Issues in Methodology and Ethics Carol Aubrey No preview available - 2000 |