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Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications
EJ. Wagenmakers, M. Marsman, T. Jamil, A. Ly, J. Verhagen, J. Love, R. Selker, QF. Gronau, M. Šmíra, S. Epskamp, D. Matzke, JN. Rouder, RD. Morey,
Language English Country United States
Document type Journal Article, Research Support, Non-U.S. Gov't
NLK
Free Medical Journals
from 2001 to 2 years ago
ProQuest Central
from 2011-02-01 to 1 year ago
Medline Complete (EBSCOhost)
from 2011-02-01 to 1 year ago
Health & Medicine (ProQuest)
from 2011-02-01 to 1 year ago
Psychology Database (ProQuest)
from 2011-02-01 to 1 year ago
- MeSH
- Bayes Theorem * MeSH
- Humans MeSH
- Psychology * MeSH
- Research Design MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to quantify evidence and monitor its progression as data come in, without needing to know the intention with which the data were collected. We end by countering several objections to Bayesian hypothesis testing. Part II of this series discusses JASP, a free and open source software program that makes it easy to conduct Bayesian estimation and testing for a range of popular statistical scenarios (Wagenmakers et al. this issue).
References provided by Crossref.org
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- $a Wagenmakers, Eric-Jan $u Department of Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 VZ, Amsterdam, The Netherlands. EJ.Wagenmakers@gmail.com.
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