Bayesian Benefits for the Pragmatic Researcher

Wagenmakers, Eric–Jan; Morey, Richard D.; Lee, Michael

doi:10.1177/0963721416643289

Cited by 304 publications

(304 citation statements)

References 22 publications

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“…It has only been in the last few decades that modern (personal) computers have allowed researchers to estimate likelihoods for alternative hypotheses using sophisticated simulation methods. Because of this newly acquired computational power, there is a robust and growing literature investigating the use of such simulation methods for applications of Bayes Theorem to experimental research (see Wagenmakers et al 2016 for a short argument in favor of Bayesian inference and recommended readings; see Mulder & Wagenmakers 2016 for an introduction to a special issue dedicated to Bayes factors in psychological research; see Rouder et al in press for a specific Bayesian analysis of factorial experimental designs). In a similar vein, in many cases the prior is Sprouse and Almeida: Design sensitivity and statistical power in acceptability judgment experiments Art.…”

Section: Bayesian Hypothesis Testingmentioning

confidence: 99%

Design sensitivity and statistical power in acceptability judgment experiments

Sprouse

Almeida

2017

Glossa: A Journal of General Linguistics

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Previous investigations into the validity of acceptability judgment data have focused almost exclusively on type I errors (or false positives) because of the consequences of such errors for syntactic theories (Sprouse & Almeida 2012;Sprouse et al. 2013). The current study complements these previous studies by systematically investigating the type II error rate (false negatives), or equivalently, the statistical power, of a wide cross-section of possible acceptability judgment experiments. Though type II errors have historically been assumed to be less costly than type I errors, the dynamics of scientific publishing mean that high type II error rates (i.e., studies with low statistical power) can lead to increases in type I error rates in a given field of study. We present a set of experiments and resampling simulations to estimate statistical power for four tasks (forced-choice, Likert scale, magnitude estimation, and yes-no), 50 effect sizes instantiated by real phenomena, sample sizes from 5 to 100 participants, and two approaches to statistical analysis (null hypothesis and Bayesian). Our goals are twofold (i) to provide a fuller picture of the status of acceptability judgment data in syntax, and (ii) to provide detailed information that syntacticians can use to design and evaluate the sensitivity of acceptability judgment experiments in their own research.

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Section: Bayesian Hypothesis Testingmentioning

confidence: 99%

Design sensitivity and statistical power in acceptability judgment experiments

Sprouse

Almeida

2017

Glossa: A Journal of General Linguistics

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“…Although useful, here we decided to focus on BHT as this is a Bayesian alternative to NHST, which is commonly used in our field. For thorough discussions of the advantages and disadvantages of different alternatives to NHST, we refer to Anderson, Burnham, and Thompson (2000); Dienes (2008); Gardner and Altman (1986) and Wagenmakers et al (2016). …”

Section: Discussionmentioning

confidence: 99%

“…whether p -values cross the level), and inferences are based on unobserved data (Cohen, 1994; Wagenmakers, 2007). Given those limitations, many have suggested alternative approaches such as model selection (Tibshirani, 1996; Yuan & Lin, 2006) or Bayesian statistics (Dienes, 2016; Wagenmakers, Morey, & Lee, 2016; Wagenmakers et al, 2017). Although each approach comes with pros and cons, here we extend on Bayesian hypothesis testing, a Bayesian alternative to NHST, and how it could be particularly useful in analysing conditioning data.…”

Section: P-values and Nhst For Threat Conditioning Datamentioning

confidence: 99%

Bayesian hypothesis testing for human threat conditioning research: an introduction and the condir R package

Krypotos

Klugkist

Engelhard

2017

European Journal of Psychotraumatology

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Threat conditioning procedures have allowed the experimental investigation of the pathogenesis of Post-Traumatic Stress Disorder. The ﬁndings of these procedures have also provided stable foundations for the development of relevant intervention programs (e.g. exposure therapy). Statistical inference of threat conditioning procedures is commonly based on p-values and Null Hypothesis Signiﬁcance Testing (NHST). Nowadays, however, there is a growing concern about this statistical approach, as many scientists point to the various limitations of p-values and NHST. As an alternative, the use of Bayes factors and Bayesian hypothesis testing has been suggested. In this article, we apply this statistical approach to threat conditioning data. In order to enable the easy computation of Bayes factors for threat conditioning data we present a new R package named condir, which can be used either via the R console or via a Shiny application. This article provides both a non-technical introduction to Bayesian analysis for researchers using the threat conditioning paradigm, and the necessary tools for computing Bayes factors easily.

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“…worse than the average have their densities decreased (see 41,66). While the discrete form of Bayes' rule has natural applications in hypothesis testing, the continuous form more naturally lends itself to parameter estimation.…”

Section: I T Ementioning

confidence: 99%