Deborah G. Mayo

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Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents,(More)
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After some general remarks about the interrelation between philosophical and statistical thinking, the discussion centres largely on significance tests. These are defined as the calculation of p-values rather than as formal procedures for “acceptance” and “rejection.” A number of types of null hypothesis are described and a principle for evidential(More)
It is not uncommon to see statistics texts argue that in frequentist theory one is faced with the following dilemma, either to deny the appropriateness of conditioning on the precision of the tool chosen by the toss of a coin, or else to embrace the strong likelihood principle which entails that frequentist sampling distributions are irrelevant to inference(More)
First, there is the authors’ recognition that methodology is ineluctably bound up with philosophy. If nothing else “strictures derived from philosophy can inhibit research progress” (p. 4). They note, for example, the reluctance of some Bayesians to test their models because of their belief that “Bayesian models were by definition subjective,” or perhaps(More)
The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve(More)
We argue that a responsible analysis of today’s evidence-based risk assessments and risk debates in biology demands a critical or metascientific scrutiny of the uncertainties, assumptions, and threats of error along the manifold steps in risk analysis. Without an accompanying methodological critique, neither sensitivity to social and ethical values, nor(More)