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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,
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, práce podpořená grantem
NLK
Free Medical Journals
od 2001 do Před 2 roky
ProQuest Central
od 2011-02-01 do Před 1 rokem
Medline Complete (EBSCOhost)
od 2011-02-01 do Před 1 rokem
Health & Medicine (ProQuest)
od 2011-02-01 do Před 1 rokem
Psychology Database (ProQuest)
od 2011-02-01 do Před 1 rokem
- MeSH
- Bayesova věta * MeSH
- lidé MeSH
- psychologie * MeSH
- výzkumný projekt MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem 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).
Citace poskytuje 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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