@article{bayarri2012,
title = {Criteria for {{Bayesian}} Model Choice with Application to Variable Selection},
author = {Bayarri, M. J. and Berger, J. O. and Forte, A. and {Garcia-Donato}, G.},
year = {2012},
journal = {The Annals of Statistics},
volume = {40},
pages = {1550--1577}
}
@article{belia2005,
title = {Researchers {{Misunderstand Confidence Intervals}} and {{Standard Error Bars}}.},
author = {Belia, Sarah and Fidler, Fiona and Williams, Jennifer and Cumming, Geoff},
year = {2005},
journal = {Psychological Methods},
volume = {10},
number = {4},
pages = {389--396},
issn = {1939-1463, 1082-989X},
doi = {10.1037/1082-989X.10.4.389},
langid = {english},
keywords = {Confidence Intervals,Data Interpretation; Statistical,Humans,Research}
}
@incollection{berger2001,
title = {Objective {{Bayesian}} Methods for Model Selection: {{Introduction}} and Comparison},
booktitle = {Model {{Selection}}},
author = {Berger, J. O. and Pericchi, L. R.},
year = {2001},
pages = {135--207},
publisher = {{Institute of Mathematical Statistics Lecture Notes - Monograph Series}},
address = {{Beachwood}}
}
@article{burkner2021,
title = {Bayesian Item Response Modeling in {{R}} with {{brms}} and {{Stan}}},
author = {B{\"u}rkner, Paul-Christian},
year = {2021},
journal = {Journal of Statistical Software},
volume = {100},
number = {5},
pages = {1--54},
doi = {10.18637/jss.v100.i05},
encoding = {UTF-8}
}
@article{button2018,
title = {Reboot Undergraduate Courses for Reproducibility},
author = {Button, Katherine},
year = {2018},
journal = {Nature},
volume = {561},
pages = {287},
doi = {10.1038/d41586-018-06692-8},
copyright = {2018 Nature},
langid = {english}
}
@article{carlin1995,
title = {Bayesian {{Model Choice Via Markov-Chain Monte-Carlo Methods}}},
author = {Carlin, Bp and Chib, S.},
year = {1995},
journal = {Journal of the Royal Statistical Society Series B-Methodological},
volume = {57},
number = {3},
pages = {473--484},
issn = {0035-9246},
abstract = {Markov chain Monte Carlo (MCMC) integration methods enable the fitting of models of virtually unlimited complexity, and as such have revolutionized the practice of Bayesian data analysis. However, comparison across models may not proceed in a completely analogous fashion, owing to violations of the conditions sufficient to ensure convergence of the Markov chain. In this paper we present a framework for Bayesian model choice, along with an MCMC algorithm that does not suffer from convergence difficulties. Our algorithm applies equally well to problems where only one model is contemplated but its proper size is not known at the outset, such as problems involving integer-valued parameters, multiple changepoints or finite mixture distributions. We illustrate our approach with two published examples.},
langid = {english},
keywords = {bayes factor,finite mixture model,gibbs,gibes sampler,integer-valued parameters,models of varying size,multiple changepoint model,nonnested models},
annotation = {WOS:A1995RE15100001}
}
@book{chen2000,
title = {Monte {{Carlo}} Methods in {{Bayesian}} Computation},
author = {Chen, M.-H. and Shao, Q.-M. and Ibrahim, J. G.},
year = {2000},
publisher = {{Springer}},
address = {{New York}},
file = {/home/jorge/Zotero/storage/R8LAG8MR/9780387989358.html}
}
@article{cohen1994,
title = {The Earth Is Round (P{$<$}.05)},
author = {Cohen, J.},
year = {1994},
journal = {The American Psychologist},
volume = {49},
pages = {997--1003}
}
@article{dienes2011,
title = {Bayesian versus Orthodox Statistics: {{Which}} Side Are You On?},
author = {Dienes, Z.},
year = {2011},
journal = {Perspectives on Psychological Science},
volume = {6},
pages = {274--290}
}
@article{dienes2014,
title = {Using {{Bayes}} to Get the Most out of Non-Significant Results},
author = {Dienes, Z.},
year = {2014},
journal = {Frontiers in Psycholology},
volume = {5},
pages = {781},
doi = {10.3389/fpsyg.2014.00781}
}
@article{dienes2016,
title = {How {{Bayes}} Factors Change Scientific Practice},
author = {Dienes, Z.},
year = {2016},
journal = {Journal of Mathematical Psychology},
volume = {72},
pages = {78--89}
}
@article{edwards1963,
title = {Bayesian Statistical Inference for Psychological Research},
author = {Edwards, W. and Lindman, H. and Savage, L. J.},
year = {1963},
journal = {Psychological Review},
volume = {70},
pages = {193--242}
}
@article{etz2018a,
title = {Introduction to {{Bayesian Inference}} for {{Psychology}}},
author = {Etz, Alexander and Vandekerckhove, Joachim},
year = {2018},
month = feb,
journal = {Psychonomic Bulletin \& Review},
volume = {25},
number = {1},
pages = {5--34},
issn = {1069-9384, 1531-5320},
doi = {10.3758/s13423-017-1262-3},
langid = {english},
keywords = {Bayesian inference and parameter estimation,Bayesian statistics,Tutorial}
}
@article{falk1995,
title = {Significance {{Tests Die Hard}}: {{The Amazing Persistence}} of a {{Probabilistic Misconception}}},
shorttitle = {Significance {{Tests Die Hard}}},
author = {Falk, Ruma and Greenbaum, Charles W.},
year = {1995},
month = feb,
journal = {Theory \& Psychology},
volume = {5},
number = {1},
pages = {75--98},
issn = {0959-3543, 1461-7447},
doi = {10.1177/0959354395051004},
abstract = {We present a critique showing the flawed logical structure of statistical significance tests. We then attempt to analyze why, in spite of this faulty reasoning, the use of significance tests persists. We identify the illusion of probabilistic proof by contradiction as a central stumbling block, because it is based on a misleading generalization of reasoning from logic to inference under uncertainty. We present new data from a student sample and examples from the psychological literature showing the strength and prevalence of this illusion. We identify some intrinsic cognitive mechanisms (similarity to modus tollens reasoning; verbal ambiguity in describing the meaning of significance tests; and the need to rule out chance findings) and extrinsic social pressures which help to maintain the illusion. We conclude by mentioning some alternative methods for presenting and analyzing psychological data, none of which can be considered the ultimate method.},
langid = {english}
}
@article{gallistel2009,
title = {The Importance of Proving the Null},
author = {Gallistel, C. R.},
year = {2009},
journal = {Psychological Review},
volume = {116},
pages = {439--453}
}
@book{gamerman2006,
title = {Markov Chain {{Monte Carlo}}: {{Stochastic}} Simulation for {{Bayesian}} Inference},
author = {Gamerman, D. and Lopes, H. F.},
year = {2006},
edition = {Second},
publisher = {{Chapman \& Hall/CRC}},
address = {{Boca Raton, FL}}
}
@article{gelman1995,
title = {Avoiding Model Selection in {{Bayesian}} Social Research},
author = {Gelman, A. and Rubin, D. B.},
year = {1995},
journal = {Sociological Methodology},
volume = {25},
pages = {165--173}
}
@article{gelman1998,
title = {Simulating Normalizing Constants: {{From}} Importance Sampling to Bridge Sampling to Path Sampling},
shorttitle = {Simulating Normalizing Constants},
author = {Gelman, A. and Meng, X. L.},
year = {1998},
month = may,
journal = {Statistical Science},
volume = {13},
number = {2},
pages = {163--185},
issn = {0883-4237},
abstract = {Computing (ratios of) normalizing constants of probability models is a fundamental computational problem for many statistical and scientific studies. Monte Carlo simulation is an effective technique, especially with complex and high-dimensional models. This paper aims to bring to the attention of general statistical audiences of some effective methods originating from theoretical physics and at the same time to explore these methods from a more statistical perspective, through establishing theoretical. connections and illustrating their uses with statistical problems. We show that the acceptance ratio method and thermodynamic integration are natural generalizations of importance sampling, which is most familiar to statistical audiences. The former generalizes importance sampling through the use of a single "bridge" density and is thus a case of bridge sampling in the sense of Meng and Wong. Thermodynamic integration, which is also known in the numerical analysis literature as Ogata's method for high-dimensional integration, corresponds to the use of infinitely many and continuously connected bridges (and thus a "path"). Our path sampling formulation offers more flexibility and thus potential efficiency to thermodynamic integration, and the search of optimal paths turns out to have close connections with the Jeffreys prior density and the Rao and Hellinger distances between two densities. We provide an informative theoretical example as well as two empirical examples (involving 17- to 70-dimensional integrations) to illustrate the potential and implementation of path sampling. We also discuss some open problems.},
langid = {english},
keywords = {acceptance ratio method,distributions,Hellinger distance,inference,integration,Jeffreys prior density,linkage,Markov chain Monte Carlo,maximum-likelihood,models,monte-carlo method,numerical integration,Rao distance,thermodynamic integration},
annotation = {WOS:000074917200006}
}
@article{goodman2008,
title = {A {{Dirty Dozen}}: {{Twelve P-Value Misconceptions}}},
shorttitle = {A {{Dirty Dozen}}},
author = {Goodman, Steven},
year = {2008},
month = jul,
journal = {Seminars in Hematology},
volume = {45},
number = {3},
pages = {135--140},
issn = {00371963},
doi = {10.1053/j.seminhematol.2008.04.003},
langid = {english},
keywords = {Probability,Research Design}
}
@misc{goodrich2022,
title = {Rstanarm: {{Bayesian}} Applied Regression Modeling via {{Stan}}.},
author = {Goodrich, Ben and Gabry, Jonah and Ali, Imad and Brilleman, Sam},
year = {2022}
}
@article{green1995,
title = {Reversible Jump {{Markov}} Chain {{Monte Carlo}} Computation and {{Bayesian}} Model Determination},
author = {Green, P. J.},
year = {1995},
month = dec,
journal = {Biometrika},
volume = {82},
number = {4},
pages = {711--732},
issn = {0006-3444},
doi = {10.1093/biomet/82.4.711},
abstract = {Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some fixed standard underlying measure. They have therefore not been available for application to Bayesian model determination, where the dimensionality of the parameter vector is typically not fixed. This paper proposes a new framework for the construction of reversible Markov chain samplers that jump between parameter subspaces of differing dimensionality, which is flexible and entirely constructive. It should therefore have wide applicability in model determination problems. The methodology is illustrated with applications to multiple change-point analysis in one and two dimensions, and to a Bayesian comparison of binomial experiments.},
langid = {english},
keywords = {change point problems,change-point analysis,image segmentation,jump diffusion,Markov chain Monte Carlo,multiple binomial experiments,multiple shrinkage,step function,voronoi tessellation},
annotation = {WOS:A1995TT22800012}
}
@article{greenland2016,
title = {Statistical Tests, {{P}} Values, Confidence Intervals, and Power: A Guide to Misinterpretations},
shorttitle = {Statistical Tests, {{P}} Values, Confidence Intervals, and Power},
author = {Greenland, Sander and Senn, Stephen J. and Rothman, Kenneth J. and Carlin, John B. and Poole, Charles and Goodman, Steven N. and Altman, Douglas G.},
year = {2016},
month = apr,
journal = {European Journal of Epidemiology},
volume = {31},
number = {4},
pages = {337--350},
issn = {0393-2990, 1573-7284},
doi = {10.1007/s10654-016-0149-3},
langid = {english},
keywords = {Confidence intervals,Hypothesis testing,Null testing,P value,Power,Significance tests,Statistical testing}
}
@article{gronau2017a,
title = {A Tutorial on Bridge Sampling},
author = {Gronau, Quentin F. and Sarafoglou, Alexandra and Matzke, Dora and Ly, Alexander and Boehm, Udo and Marsman, Maarten and Leslie, David S. and Forster, Jonathan J. and Wagenmakers, E.-J. and Steingroever, Helen},
year = {2017},
month = dec,
journal = {Journal of Mathematical Psychology},
volume = {81},
pages = {80--97},
issn = {0022-2496},
doi = {10.1016/j.jmp.2017.09.005},
abstract = {The marginal likelihood plays an important role in many areas of Bayesian statistics such as parameter estimation, model comparison, and model averaging. In most applications, however, the marginal likelihood is not analytically tractable and must be approximated using numerical methods. Here we provide a tutorial on bridge sampling (Bennett, 1976; Meng \& Wong, 1996), a reliable and relatively straightforward sampling method that allows researchers to obtain the marginal likelihood for models of varying complexity. First, we introduce bridge sampling and three related sampling methods using the beta-binomial model as a running example. We then apply bridge sampling to estimate the marginal likelihood for the Expectancy Valence (EV) model a popular model for reinforcement learning. Our results indicate that bridge sampling provides accurate estimates for both a single participant and a hierarchical version of the EV model. We conclude that bridge sampling is an attractive method for mathematical psychologists who typically aim to approximate the marginal likelihood for a limited set of possibly high-dimensional models. (C) 2017 The Authors. Published by Elsevier Inc.},
langid = {english},
keywords = {Bayes factor,bayes factor,cognitive models,Hierarchical model,iowa gambling task,linear mixed models,Marginal likelihood,Normalizing constant,parameter,participants,perspective,Predictive accuracy,Reinforcement learning,signal-detection,special-issue,statistics},
annotation = {WOS:000416193800006}
}
@article{gronau2020,
title = {{{bridgesampling}}: {{An R}} Package for Estimating Normalizing Constants},
author = {Gronau, Quentin F. and Singmann, Henrik and Wagenmakers, Eric-Jan},
year = {2020},
journal = {Journal of Statistical Software},
volume = {92},
number = {10},
pages = {1--29},
doi = {10.18637/jss.v092.i10}
}
@manual{gu2021,
type = {Manual},
title = {Bain: {{Bayes}} Factors for Informative Hypotheses},
author = {Gu, Xin and Hoijtink, Herbert and Mulder, Joris and {van Lissa}, Caspar J},
year = {2021}
}
@article{haller2002,
title = {Misinterpretations of Significance: {{A}} Problem Students Share with Their Teachers?},
author = {Haller, H and Kraus, S},
year = {2002},
journal = {Methods of Psychological Research},
volume = {7},
keywords = {Comprehension,Psychologists,Statistical Significance,Statistics,Teaching},
annotation = {https://psycnet.apa.org/record/2002-14044-001}
}
@article{hoekstra2014,
title = {Robust Misinterpretation of Confidence Intervals},
author = {Hoekstra, Rink and Morey, Richard D. and Rouder, Jeffrey N. and Wagenmakers, Eric-Jan},
year = {2014},
month = oct,
journal = {Psychonomic Bulletin \& Review},
volume = {21},
number = {5},
pages = {1157--1164},
issn = {1069-9384, 1531-5320},
doi = {10.3758/s13423-013-0572-3},
langid = {english},
keywords = {Confidence intervals,Inference,Significance testing}
}
@techreport{ikeda2019,
type = {Preprint},
title = {Questionable Research Practices Following Pre-Registration},
author = {Ikeda, Ayumi and Xu, Haoqin and Fuji, Naoto and Zhu, Siqi and Yamada, Yuki},
year = {2019},
month = jan,
institution = {{PsyArXiv}},
doi = {10.31234/osf.io/b8pw9}
}
@book{jeffreys1939,
title = {Theory of Probability},
author = {Jeffreys, H.},
year = {1939},
edition = {First},
publisher = {{The Clarendon Press}},
address = {{Oxford}}
}
@book{jeffreys1961,
title = {Theory of Probability},
author = {Jeffreys, H.},
year = {1961},
edition = {Third},
publisher = {{Oxford University Press}},
address = {{Oxford}}
}
@article{john2012,
title = {Measuring the {{Prevalence}} of {{Questionable Research Practices With Incentives}} for {{Truth Telling}}},
author = {John, Leslie K. and Loewenstein, George and Prelec, Drazen},
year = {2012},
journal = {Psychological Science},
volume = {23},
number = {5},
pages = {524--532},
issn = {0956-7976, 1467-9280},
doi = {10.1177/0956797611430953},
langid = {english}
}
@article{kass1993,
title = {Bayes Factors in Practice},
author = {Kass, R. E.},
year = {1993},
journal = {The Statistician},
volume = {42},
pages = {551--560}
}
@article{kass1995,
title = {Bayes Factors},
author = {Kass, R. E. and Raftery, A. E.},
year = {1995},
journal = {Journal of the American Statistical Association},
volume = {90},
pages = {773--795},
doi = {10.2307/2291091}
}
@book{lee2013,
ids = {leeBayesianCognitiveModeling2013},
title = {Bayesian Cognitive Modeling: A Practical Course},
shorttitle = {Bayesian Cognitive Modeling},
author = {Lee, Michael D. and Wagenmakers, Eric-Jan},
year = {2013},
publisher = {{Cambridge University Press}},
address = {{Cambridge ; New York}},
isbn = {978-1-107-01845-7 978-1-107-60357-8},
lccn = {BF311 .L38 2013},
keywords = {Bayesian statistical decision theory,Cognitive science,Mathematical models},
annotation = {OCLC: ocn861318341}
}
@article{liu2008,
title = {Bayes Factors: {{Prior}} Sensitivity and Model Generalizability},
author = {Liu, C. C. and Aitkin, M.},
year = {2008},
journal = {Journal of Mathematical Psychology},
volume = {52},
pages = {362--375}
}
@article{ludecke2022,
title = {Easystats: {{Framework}} for Easy Statistical Modeling, Visualization, and Reporting},
author = {L{\"u}decke, Daniel and {Ben-Shachar}, Mattan S. and Patil, Indrajeet and Wiernik, Brenton M. and Makowski, Dominique},
year = {2022},
journal = {CRAN}
}
@article{ly2016,
title = {Harold {{Jeffreys}}'s Default {{Bayes}} Factor Hypothesis Tests: {{Explanation}}, Extension, and Application in Psychology},
author = {Ly, A. and Verhagen, J. and Wagenmakers, E.-J.},
year = {2016},
journal = {Journal of Mathematical Psychology},
volume = {72},
pages = {19--32}
}
@article{makowski2019,
title = {{{bayestestR}}: {{Describing}} Effects and Their Uncertainty, Existence and Significance within the Bayesian Framework.},
author = {Makowski, Dominique and {Ben-Shachar}, Mattan S. and L{\"u}decke, Daniel},
year = {2019},
journal = {Journal of Open Source Software},
volume = {4},
number = {40},
pages = {1541},
doi = {10.21105/joss.01541}
}
@article{marden2000,
title = {Hypothesis {{Testing}}: {{From}} p Values to {{Bayes}} Factors},
author = {Marden, J. I.},
year = {2000},
journal = {Journal of the American Statistical Association},
volume = {95},
pages = {1316--1320}
}
@article{masson2011,
title = {A Tutorial on a Practical {{Bayesian}} Alternative to Null\textendash Hypothesis Significance Testing},
author = {Masson, M. E. J.},
year = {2011},
journal = {Behavior Research Methods},
volume = {43},
pages = {679--690}
}
@article{morey2016,
title = {The Philosophy of {{Bayes}} Factors and the Quantification of Statistical Evidence},
author = {Morey, R. D. and Romeijn, Jan-Willem and Rouder, Jeffrey N.},
year = {2016},
journal = {Journal of Mathematical Psychology},
series = {Bayes {{Factors}} for {{Testing Hypotheses}} in {{Psychological Research}}: {{Practical Relevance}} and {{New Developments}}},
volume = {72},
pages = {6--18},
issn = {0022-2496},
doi = {10.1016/j.jmp.2015.11.001},
abstract = {A core aspect of science is using data to assess the degree to which data provide evidence for competing claims, hypotheses, or theories. Evidence is by definition something that should change the credibility of a claim in a reasonable person's mind. However, common statistics, such as significance testing and confidence intervals have no interface with concepts of belief, and thus it is unclear how they relate to statistical evidence. We explore the concept of statistical evidence, and how it can be quantified using the Bayes factor. We also discuss the philosophical issues inherent in the use of the Bayes factor.},
keywords = {Bayes factor,Hypothesis testing}
}
@article{nickerson2000a,
title = {Null Hypothesis Statistical Testing: {{A}} Review of an Old and Continuing Controversy},
author = {Nickerson, R. S.},
year = {2000},
journal = {Psychological Methods},
volume = {5},
pages = {241--301}
}
@book{oakes1986,
title = {Statistical Inference: {{A}} Commentary for the Social and Behavioural Sciences},
author = {Oakes, Michael W.},
year = {1986},
publisher = {{John Wiley \& Sons}},
address = {{Chicester}}
}
@article{robert2016,
title = {The Expected Demise of the {{Bayes}} Factor},
author = {Robert, CP},
year = {2016},
journal = {Journal of Mathematical Psychology},
number = {Query date: 2021-12-22 15:03:02},
publisher = {{Elsevier}},
abstract = {\ldots{} Furthermore, the setting is also ``ideal'' in that the Bayes factor simplifies \ldots{} Bayesian test[ing procedure]. Once again, we thus are in a setting where Bayesian and frequentist answers are in one-to-one correspondence (at least for a fixed sample size) and where the Bayes factor \ldots}
}
@article{rouder2009,
title = {Bayesian t Tests for Accepting and Rejecting the Null Hypothesis},
author = {Rouder, J. N. and Speckman, P. L. and Sun, D. and Morey, R. D.},
year = {2009},
journal = {Psychonomic Bulletin \& Review},
volume = {16},
pages = {225--237},
keywords = {Akaike Information Criterion,Marginal Likelihood,Posterior Odds,Prior Standard Deviation,Subliminal Priming}
}
@article{rouder2014,
title = {Optional Stopping: {{No}} Problem for {{Bayesians}}},
shorttitle = {Optional Stopping},
author = {Rouder, J. N.},
year = {2014},
journal = {Psychonomic Bulletin \& Review},
volume = {21},
number = {2},
pages = {301--308},
issn = {1531-5320},
doi = {10.3758/s13423-014-0595-4},
langid = {english},
keywords = {Bayes factors,Bayesian testing,Optional stopping,p-hacking,Statistics}
}
@article{simmons2011,
ids = {simmons2011a,simmonsFalsepositivePsychologyUndisclosed2011},
title = {False-{{Positive Psychology}}: {{Undisclosed Flexibility}} in {{Data Collection}} and {{Analysis Allows Presenting Anything}} as {{Significant}}},
author = {Simmons, Joseph P. and Nelson, Leif D. and Simonsohn, Uri},
year = {2011},
journal = {Psychological Science},
volume = {22},
number = {11},
pages = {1359--1366},
issn = {0956-7976},
doi = {10.1177/0956797611417632},
langid = {english}
}
@article{tendeiro2019,
title = {A Review of Issues about Null Hypothesis {{Bayesian}} Testing.},
author = {Tendeiro, JN and Kiers, HAL},
year = {2019},
journal = {Psychological methods},
number = {Query date: 2021-12-22 15:03:02},
publisher = {{psycnet.apa.org}},
abstract = {\ldots{} the Bayes factor in research applications, considered the Bayesian alternative to NHST, is also increasing, we conducted a small-scale search on Google Scholar using the terms ``Bayesian test,'' ``null hypothesis,'' and ``psychology\ldots{} of Bayesian testing in psychology, which indeed \ldots},
keywords = {Hypothesis Testing,Models,Null Hypothesis Testing,Statistical Probability,Statistical Significance,Statistics}
}
@article{tendeiro2022b,
author = {J. N. Tendeiro and H. A. L. Kiers and D. van Ravenzwaaij},
title = {Worked-out examples of the adequacy of Bayesian optional stopping},
journal = {Psychonomic Bulletin & Review},
year = {2022},
volume = {29},
pages = {70-87},
doi = {10.3758/s13423-021-01962-5},
note = {Preprint: \url{https://psyarxiv.com/9t2e7/}},
}
@article{tendeiro2023a,
author = {J. N. Tendeiro and H. A. L. Kiers},
title = {On the white, the black, and the many shades of gray in between: Our reply to van Ravenzwaaij and Wagenmakers (2021)},
year = {2023},
note = {In press, Psychological Methods. Preprint: \url{https://psyarxiv.com/tjxvz/}}
}
@article{tendeiro2023b,
author = {J. N. Tendeiro and H. A. L. Kiers},
title = {With Bayesian estimation one can get all that Bayes factors offer, and more},
year = {2023},
journal = {In press, Psychonomic Bulletin & Review},
doi = {10.3758/s13423-022-02164-3},
note = {Preprint: \url{https://psyarxiv.com/zbpmy/}}
}
@article{vanpaemel2010,
title = {Prior Sensitivity in Theory Testing: {{An}} Apologia for the {{Bayes}} Factor},
author = {Vanpaemel, Wolf},
year = {2010},
journal = {Journal of Mathematical Psychology},
volume = {54},
pages = {491--498}
}
@article{wagenmakers2007,
title = {A Practical Solution to the Pervasive Problems Ofp Values},
author = {Wagenmakers, Eric-Jan},
year = {2007},
month = oct,
journal = {Psychonomic Bulletin \& Review},
volume = {14},
number = {5},
pages = {779--804},
issn = {1069-9384, 1531-5320},
doi = {10.3758/BF03194105},
langid = {english},
keywords = {Bayesian Information Criterion,Null Hypothesis,Posterior Probability,Prior Distribution,Statistical Inference}
}
@article{wagenmakers2010,
title = {Bayesian Hypothesis Testing for Psychologists: {{A}} Tutorial on the {{Savage}}\textendash{{Dickey}} Method},
author = {Wagenmakers, EJ and Lodewyckx, T and Kuriyal, H and {...}},
year = {2010},
journal = {\ldots{} psychology},
number = {Query date: 2021-12-22 15:03:02},
publisher = {{Elsevier}},
abstract = {\ldots{} is the focus of this article\textemdash is that the marginal likelihood and the Bayes factor are often quite difficult to compute. Earlier, we saw that with a \ldots{} Our Bayesian test of the both-primed benefit proceeds as follows. We start by assuming that for participant i the number of correct choices \ldots},
keywords = {Bayes factor,Hierarchical modeling,Hierarchical modeling,linear-models,marginal likelihood,markov-chain,Model selection,model uncertainty,null hypothesis,Order-restrictions,p-values,parameters,Random effects,signal-detection,Statistical evidence,t-test,weighted likelihood ratio}
}
@article{wagenmakers2018,
title = {Bayesian Inference for Psychology. {{Part I}}: {{Theoretical}} Advantages and Practical Ramifications},
author = {Wagenmakers, EJ and Marsman, M and Jamil, T and Ly, A and {...}},
year = {2018},
journal = {Psychonomic bulletin \& \ldots},
number = {Query date: 2021-12-22 15:03:02},
publisher = {{Springer}},
doi = {10.3758/s13423-017-1343-3},
abstract = {\ldots{} The Bayes factor hypothesis test compares the predictive adequacy of two competing \ldots{} advantages that Bayesian parameter estimation and Bayes factor hypothesis tests have to offer it \ldots{} Next we briefly address a series of ten objections against the Bayes factor hypothesis test. Our \ldots}
}
@article{withers2002,
title = {Quantitative Methods: {{Bayesian}} Inference, {{Bayesian}} Thinking},
author = {Withers, S. D.},
year = {2002},
journal = {Progress in Human Geography},
volume = {26},
pages = {553--566}
}
@article{wong2022,
title = {On the {{Potential Mismatch Between}} the {{Function}} of the {{Bayes Factor}} and {{Researchers}}' {{Expectations}}},
author = {Wong, Tsz Keung and Kiers, Henk and Tendeiro, Jorge},
year = {2022},
month = jun,
journal = {Collabra: Psychology},
volume = {8},
number = {1},
pages = {36357},
issn = {2474-7394},
doi = {10.1525/collabra.36357},
abstract = {The aim of this study is to investigate whether there is a potential mismatch between the usability of a statistical tool and psychology researchers' expectation of it. Bayesian statistics is often promoted as an ideal substitute for frequentists statistics since it coincides better with researchers' expectations and needs. A particular incidence of this is the proposal of replacing Null Hypothesis Significance Testing (NHST) by Null Hypothesis Bayesian Testing (NHBT) using the Bayes factor. In this paper, it is studied to what extent the usability and expectations of NHBT match well. First, a study of the reporting practices in 73 psychological publications was carried out. It was found that eight Questionable Reporting and Interpreting Practices (QRIPs) occur more than once among the practitioners when doing NHBT. Specifically, our analysis provides insight into possible mismatches and their occurrence frequencies. A follow-up survey study has been conducted to assess such mismatches. The sample (N = 108) consisted of psychology researchers, experts in methodology (and/or statistics), and applied researchers in fields other than psychology. The data show that discrepancies exist among the participants. Interpreting the Bayes Factor as posterior odds and not acknowledging the notion of relative evidence in the Bayes Factor are arguably the most concerning ones. The results of the paper suggest that a shift of statistical paradigm cannot solve the problem of misinterpretation altogether if the users are not well acquainted with the tools.},
langid = {english}
}