Generalizing across auxiliary, statistical, and inferential assumptions

Datos Bibliográficos

ID8975627
AutoresDavid Trafimow (0000-0002-0788-8044, Psychology New Mexico State University Las Cruces NM US, autor de correspondencia)
Año2021
Volumen52
Número1
Páginas37-48
Fecha de publicación2021-05-15
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaJournal for the Theory of Social Behaviour (JOURNAL)
Identificadores de la revistaISSN: 0021-8308 • E-ISSN: 1468-5914
EditorialWiley (PUBLISHER • GB)
DOI10.1111/jtsb.12296
OpenAlexW3161955129
IdiomaEN
Citas recibidas3
Referencias citadas29

There is a long history of discussion about the ability of researchers to generalize their findings. But findings are not the only entity that researchers can attempt to generalize. Scientists have theories, empirical hypotheses, and statistical hypotheses too. The extent to which scientists can generalize these is an open issue. As a prerequisite to proposals about generalizing theories, empirical hypotheses, and statistical hypotheses; they must be distinguished from each other. Another prerequisite is to specify, across what, the researcher wishes to generalize. I show that theories may or may not generalize across auxiliary assumptions, empirical hypotheses may or may not generalize across statistical assumptions, and statistical hypotheses may or may not generalize across inferential assumptions. The reasoning to be presented, pertaining to generalization, militates against the typical practice of using p ‐values to draw conclusions about hypotheses

Empirical research · Epistemology · Generalization · Inference · Statistical hypothesis testing · Statistical inference · Statistical model · Statistics · Artificial Intelligence · Bayesian Modeling and Causal Inference · Computer Science · Mathematics · Philosophy and History of Science · Psychology · Statistical Methods and Bayesian Inference