Likelihood: An Account of the Statistical Concept of Likelihood and Its Application to Scientific Inference. A. W. F. Edwards

1974 ◽  
Vol 41 (4) ◽  
pp. 427-429
Author(s):  
Charles G. Morgan
Keyword(s):  
Statistical Concept ◽  
Scientific Inference

Likelihood – an Account of the Statistical Concept and its Application to Scientific Inference

1972 ◽  
Vol 23 (8) ◽  
pp. 490-490
Author(s):  
D A Budgett
Keyword(s):  
Statistical Concept ◽  
Scientific Inference

Likelihood: An Account of the Statistical Concept of Likelihood and Its Application to Scientific Inference

1973 ◽  
Vol 57 (402) ◽  
pp. 357
Author(s):  
J. H. Durran ◽  
A. W. F. Edwards
Keyword(s):  
Statistical Concept ◽  
Scientific Inference

Pyramid Power: Statistics as Part of Greater Scientific Inference

2003 ◽  
Vol 48 (1) ◽  
pp. 117-120
Author(s):  
Dana S. Dunn
Keyword(s):  
Scientific Inference

The Foundations Of Scientific Inference

1967 ◽  
Author(s):  
Wesley C. Salmon
Keyword(s):  
Scientific Inference

On Purely Probabilistic Theories of Scientific Inference

1975 ◽  
Vol 42 (3) ◽  
pp. 242-249 ◽  
Author(s):  
David G. Blair
Keyword(s):  
Scientific Inference ◽  
Probabilistic Theories

scholarly journals How Computational Modeling Can Force Theory Building in Psychological Science

2021 ◽  
pp. 174569162097058
Author(s):  
Olivia Guest ◽  
Andrea E. Martin
Keyword(s):  
Computational Modeling ◽  
Computational Model ◽  
Process Modeling ◽  
Theory Building ◽  
Formal Modeling ◽  
Inference Process ◽  
Experimental Practice ◽  
Psychological Science ◽  
Scientific Inference ◽  
And Behaviors

Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, specify, and formalize intuitions that otherwise remain unexamined—what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all.

scholarly journals The Limited Role of Formal Statistical Inference in Scientific Inference

2019 ◽  
Vol 73 (sup1) ◽  
pp. 91-98 ◽  
Author(s):  
Raymond Hubbard ◽  
Brian D. Haig ◽  
Rahul A. Parsa
Keyword(s):  
Statistical Inference ◽  
Scientific Inference ◽  
Limited Role
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