Statistical Principles

Charles A. Rohde
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Charles A. Rohde: Johns Hopkins University, Bloomberg School of Health

Chapter Chapter 13 in Introductory Statistical Inference with the Likelihood Function, 2014, pp 151-165 from Springer

Abstract: Abstract A number of principles for evaluating the evidence (information) provided by data have been formulated: The repeated sampling principle: evidence (information) is evaluated using hypothetical repeated sampling. The sufficiency principle: evidence (information) should depend only on the value of a sufficient statistic. The conditionality principle: evidence (information) should depend only on the experiment actually performed. The likelihood principle: evidence (information) resulting from observations with proportional likelihoods should be the same. The Bayesian coherency principle: evidence (information) from data is used to obtain (beliefs) using Bayes theorem which requires consistent (coherent) betting behavior. Birnbaum’s confidence concept: a concept of statistical evidence is not plausible unless it finds “strong evidence” for H 2 as against H 1 with small probability (α) when H 1 is true and with much larger probability (1 −β) when H 2 is true.

Keywords: Sufficiency Principle; Conditioning Principles; Likelihood Principle; Proportional Likelihood; Stopping Rule Principle (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-10461-4_13

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DOI: 10.1007/978-3-319-10461-4_13

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