- Record: found
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Eric-Jan Wagenmakers ^{ , } ^{1} ,
Maarten Marsman ^{1} ,
Tahira Jamil ^{1} ,
Alexander Ly ^{1} ,
Josine Verhagen ^{1} ,
Jonathon Love ^{1} ,
Ravi Selker ^{1} ,
Quentin F. Gronau ^{1} ,
Martin Šmíra ^{2} ,
Sacha Epskamp ^{1} ,
Dora Matzke ^{1} ,
Jeffrey N. Rouder ^{3} ,
Richard D. Morey ^{4}

4 August 2017

Hypothesis test, Statistical evidence, Bayes factor, Posterior distribution

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).

- Record: found
- Abstract: not found
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J P Huelsenbeck, F Ronquist, Naftali Kaminski (2002)

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Robert E. Kass, Adrian E. Raftery (1995)

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John Ioannidis (2005)

Springer US
(New York
)

1069-9384

1531-5320

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.