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.
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