## 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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actions actually alternative analysis answer applications Bayesian better called Chapter choosing common concept conditional confidence interval consists critical density depends determined draw effect equal error estimator evidence in favor evidence supporting evidential example Exercise expected experiment explained factor Figure give given greater heads hypothesis hypothesis testing implies important independent interest interpretation law of likelihood least less likelihood function likelihood ratio mean measure methods misleading Neyman Neyman-Pearson normal nuisance null hypothesis objective observations obtained odds ratio outcome p-value paradigm parameter possible prior probability probability distribution problem procedure produce question random variable reason rejection represent respective rule sample scientific shows significance significance tests simple specified standard statistical strength strong evidence stronger success Suppose Table theory tion tosses treatment trials true versus weak white balls

### 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 |