Final Collapse of the Neyman-Pearson Decision Theoretic Framework and Rise of the neoFisherian

By S. Hurlbert, Celia M. Lombardi. 2009

273 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/8f95ee3e19a50adf1772aec986ebb85cd26d2f6f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. David Trafimow
2. Valentin Amrhein
3. Sander Greenland
4. P. Beninger
5. Stelios Katsanevakis
6. I. Boldina
7. Roger Mundry
8. Andrew Gelman

In Renewable Energy, Sustainability and the Environment

1. Sander Greenland
2. P. Beninger
3. Stelios Katsanevakis
4. I. Boldina
5. Roger Mundry

Citations in the corpus

• Coup de Grâce for a Tough Old Bull: “Statistically Significant” Expires (2019, background): “Hurlbert and Lombardi (2009) thoroughly reviewed the history of “statistically significant,” explicitly recommended its abandonment, and anticipated virtually all the conclusions of the ASA statement.”
• Strengthening statistical usage in marine ecology (2012, methodology): “Hurlbert and Lombardi (2009) advocate the use of ‘Neo-Fisherian Significance Assessments (NFSA)’ to overcome the problems of, and replace, the ‘paleo-Fisherian and Neyman–Pearsonian paradigms’ (i.e. NHST).”
• Valid P-Values Behave Exactly as They Should: Some Misleading Criticisms of P-Values and Their Resolution With S-Values (2019, background): “…distortive scaling and unwarranted dichotomization of P-values, the misleading and inconsistent jargon surrounding their definitions and description, and the misplaced criticisms which blame P-values for ill-conceived traditions surrounding their use and interpretation (Hurlbert and Lombardi 2009).”
• Abandon Statistical Significance (2017, background): “Indeed, this was advocated by R. A. Fisher himself (Fisher, 1956; Greenland and Poole, 2013) as well as by other early and eminent statisticians including Karl Pearson (Hurlbert and Lombardi, 2009), David Cox (Cox, 1977, 1982), and Erich Lehmann (Lehmann, 1993; Senn, 2001).”
• Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication (2018, background): “…and direct presentation of precise P-values—including Pvalues for alternative hypotheses (Poole 1987a, 1987b; Cohen 1994; Ziliak and McCloskey 2008; Hurlbert and Lombardi 2009; Amrhein, Korner-Nievergelt, and Roth 2017; Greenland 2017; Amrhein and Greenland 2018; Trafimow et al. 2018; Greenland…”
• Issues in information theory-based statistical inference—a commentary from a frequentist’s perspective (2010, background): “…null hypothesis; but see below) based on the P value being below or not below a certain threshold (usually 0.05), others have proposed to treat P values as continuous measures of evidence against the null hypothesis (reviewed in, e.g. Stoehr 1999; Quinn and Keough 2002; Hurlbert and Lombardi 2009).”