Testing Fisher, Neyman, Pearson, and Bayes

By Ronald Christensen. 2005

128 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/65c3073badcb8d74966477e28b026c5677126ef2

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Citations in the corpus

Coup de Grâce for a Tough Old Bull: “Statistically Significant” Expires (2019, methodology): “(Christensen 2005) “We will advocate discarding this [Neyman-Pearsonian] framework for most significance testing situations and replacing it with an explicitly neoFisherian one that 1) does not fix α, 2) does not describe P values as ‘significant’ or ‘non-significant,’ 3) does not accept null…”
LOPSIDED REASONING ON LOPSIDED TESTS AND MULTIPLE COMPARISONS (2012, methodology): “The true FDR = V/R for a given set of tests is always unknown, but just as one can define a maximum acceptable potential SWER (without knowing the true SWER) and use it to adjust significance criteria, one can define q, a maximum acceptable potential FDR, and use it to adjust significance criteria.”
The need for Bayesian hypothesis testing in psychological science (2017, background): “For details on the confusion between the two paradigms see Berger (2003), Christensen (2005), Hubbard and Bayarri (2003).”
Calibrated Bayes (2006, background): “the various forms of hypothesis testing that lends support to this position, see Christensen (2005). This is not to claim that calibrated Bayes solves all the problems of statistical inference.”
Bayesian clinical trials (2006, methodology): “Frequentist approach: the basics When I use the term 'frequentist' I mean the Neyman–Pearson approac”
Strengthening statistical usage in marine ecology (2012, background): “…of statistics in virtually all fields of research (e.g. Anderson et al., 2001; Cohen, 1994; Cumming and Finch, 2005; Hubbard and Bayarri, 2003; Huck, 2011; Morrison and Henkel, 1970; Sellke et al., 2001; Ziliak and McCloskey, 2008a), lending credence to Mark Twain's (1907) summary verdict…”
A practical solution to the pervasive problems ofp values (2007, methodology): “The statistical world has always been dominated by two superpowers: the Bayesians and the frequentists (see, e.g., Bayarri & Berger, 2004; Berger, 2003; Christensen, 2005; R. T. Cox, 1946; Efron, 2005; Lindley & Phillips, 1976; Neyman, 1977).”
STATISTICAL INFERENCE AT WORK: STATISTICAL PROCESS CONTROL AS AN EXAMPLE (2008, methodology): “…SPC shows more similarities to Fisher’s view on hypothesis testing than that of Pearson and Neyman, because Fisher did not require alternative hypotheses to be specified in advance (for the different views on hypothesis testing see for example Batanero, 2000; Biehler, 1982; Christensen, 2005).”
Bayesian hypothesis testing for psychologists: a tutorial on the Savage-Dickey method. (2010, unknown): “1 For more information about the difference between the three statistical paradigms, see for instance Christensen (2005),”
Final Collapse of the Neyman-Pearson Decision Theoretic Framework and Rise of the neoFisherian (2009, background): “…Daniel 1990, Freedman et al. 1991, Wilkinson & TFSI 1999, Salsburg 1992, 1993, Huberty 1993, Wang 1993, Frick 1996, Rossi 1997, Stoehr 1999, Kline 2004, Christensen 2005, Hubbard & Armstrong 2006, Fidler et al. 2006), editors, textbook writers, and researchers have yet to give them much credence.”