Bayesian hypothesis testing: Editorial to the special issue on Bayesian data analysis

Herbert Hoijtink, Sy Miin Chow

Research output: Contribution to journalEditorial

2 Citations (Scopus)

Abstract

In the past 20 years, there has been a steadily increasing attention and demand for Bayesian data analysis across multiple scientific disciplines, including psychology. Bayesian methods and the related Markov chain Monte Carlo sampling techniques offered renewed ways of handling old and challenging new problems that may be difficult or impossible to handle using classical approaches. Yet, such opportunities and potential improvements have not been sufficiently explored and investigated. This is 1 of 2 special issues in Psychological Methods dedicated to the topic of Bayesian data analysis, with an emphasis on Bayesian hypothesis testing, model comparison, and general guidelines for applications in psychology. In this editorial, we provide an overview of the use of Bayesian methods in psychological research and a brief history of the Bayes factor and the posterior predictive p value. Translational abstracts that summarize the articles in this issue in very clear and understandable terms are included in the Appendix.

Original languageEnglish (US)
Pages (from-to)211-216
Number of pages6
JournalPsychological Methods
Volume22
Issue number2
DOIs
StatePublished - Jun 2017

Fingerprint

Bayes Theorem
Psychology
Markov Chains
Guidelines
Research

All Science Journal Classification (ASJC) codes

  • Psychology (miscellaneous)

Cite this

@article{900f71cb0d274a87ac6d2c13c47519f6,
title = "Bayesian hypothesis testing: Editorial to the special issue on Bayesian data analysis",
abstract = "In the past 20 years, there has been a steadily increasing attention and demand for Bayesian data analysis across multiple scientific disciplines, including psychology. Bayesian methods and the related Markov chain Monte Carlo sampling techniques offered renewed ways of handling old and challenging new problems that may be difficult or impossible to handle using classical approaches. Yet, such opportunities and potential improvements have not been sufficiently explored and investigated. This is 1 of 2 special issues in Psychological Methods dedicated to the topic of Bayesian data analysis, with an emphasis on Bayesian hypothesis testing, model comparison, and general guidelines for applications in psychology. In this editorial, we provide an overview of the use of Bayesian methods in psychological research and a brief history of the Bayes factor and the posterior predictive p value. Translational abstracts that summarize the articles in this issue in very clear and understandable terms are included in the Appendix.",
author = "Herbert Hoijtink and Chow, {Sy Miin}",
year = "2017",
month = "6",
doi = "10.1037/met0000143",
language = "English (US)",
volume = "22",
pages = "211--216",
journal = "Psychological Methods",
issn = "1082-989X",
publisher = "American Psychological Association Inc.",
number = "2",

}

Bayesian hypothesis testing : Editorial to the special issue on Bayesian data analysis. / Hoijtink, Herbert; Chow, Sy Miin.

In: Psychological Methods, Vol. 22, No. 2, 06.2017, p. 211-216.

Research output: Contribution to journalEditorial

TY - JOUR

T1 - Bayesian hypothesis testing

T2 - Editorial to the special issue on Bayesian data analysis

AU - Hoijtink, Herbert

AU - Chow, Sy Miin

PY - 2017/6

Y1 - 2017/6

N2 - In the past 20 years, there has been a steadily increasing attention and demand for Bayesian data analysis across multiple scientific disciplines, including psychology. Bayesian methods and the related Markov chain Monte Carlo sampling techniques offered renewed ways of handling old and challenging new problems that may be difficult or impossible to handle using classical approaches. Yet, such opportunities and potential improvements have not been sufficiently explored and investigated. This is 1 of 2 special issues in Psychological Methods dedicated to the topic of Bayesian data analysis, with an emphasis on Bayesian hypothesis testing, model comparison, and general guidelines for applications in psychology. In this editorial, we provide an overview of the use of Bayesian methods in psychological research and a brief history of the Bayes factor and the posterior predictive p value. Translational abstracts that summarize the articles in this issue in very clear and understandable terms are included in the Appendix.

AB - In the past 20 years, there has been a steadily increasing attention and demand for Bayesian data analysis across multiple scientific disciplines, including psychology. Bayesian methods and the related Markov chain Monte Carlo sampling techniques offered renewed ways of handling old and challenging new problems that may be difficult or impossible to handle using classical approaches. Yet, such opportunities and potential improvements have not been sufficiently explored and investigated. This is 1 of 2 special issues in Psychological Methods dedicated to the topic of Bayesian data analysis, with an emphasis on Bayesian hypothesis testing, model comparison, and general guidelines for applications in psychology. In this editorial, we provide an overview of the use of Bayesian methods in psychological research and a brief history of the Bayes factor and the posterior predictive p value. Translational abstracts that summarize the articles in this issue in very clear and understandable terms are included in the Appendix.

UR - http://www.scopus.com/inward/record.url?scp=85020471848&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85020471848&partnerID=8YFLogxK

U2 - 10.1037/met0000143

DO - 10.1037/met0000143

M3 - Editorial

C2 - 28594223

AN - SCOPUS:85020471848

VL - 22

SP - 211

EP - 216

JO - Psychological Methods

JF - Psychological Methods

SN - 1082-989X

IS - 2

ER -