What Are the Odds? A Practical Guide to Computing and Reporting Bayes Factors

By Andrew F. Jarosz, Jennifer Wiley. Journal of Problem Solving, 2014

1,050 citations recorded by the source corpus.

The purpose of this paper is to provide an easy template for the inclusion of the Bayes factor in reporting experimental results, particularly as a recommendation for articles in the Journal of Problem Solving. The Bayes factor provides information with a similar purpose to the p-value – to allow the researcher to make statistical inferences from data provided by experiments. While the p-value is widely used, the Bayes factor provides several advantages, particularly in that it allows the researcher to make a statement about the alternative hypothesis, rather than just the null hypothesis. In addition, it provides a clearer estimate of the amount of evidence present in the data. Building on previous work by authors such as Wagenmakers (2007), Rouder et al. (2009), and Masson (2011), this article provides a short introduction to Bayes factors, before providing a practical guide to their computation using examples from published work on problem solving.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/41c493b1b0d2e77cec1906bc06b34eb2ce68db93

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Citations in the corpus

A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP (2019, positive): “A series of recent articles show how the Bayesian framework can supplement or supplant the frequentist p-value (e.g, Burton, Gurrin, & Campbell, 1998; Dienes & McLatchie, 2018; Jarosz & Wiley, 2014; Masson, 2011; Nathoo & Masson, 2016; Rouder et al., 2016).”
Indices of Effect Existence and Significance in the Bayesian Framework (2019, background): “Aside from having a straightforward interpretation (“given the observed data, is the null hypothesis of an absence of an effect more, or less likely?”), they allow to quantify the evidence in favor of the null hypothesis (Dienes, 2014; Jarosz and Wiley, 2014).”
A randomized controlled trial to test the effect of multispecies probiotics on cognitive reactivity to sad mood. (2015, background): “|D) and p(H1|D), assuming a ‘‘unit information prior’’ (for further details, see Kass and Wasserman (1995); see also Jarosz and Wiley (2014)).”
Motor Interference, But Not Sensory Interference, Increases Midfrontal Theta Activity and Brain Synchronization during Reactive Control (2021, background): “It has been suggested that BF. 10 represents strong evidence for the alternative hypothesis, while BF, 0.10 indicates strong evidence for the null hypothesis (Jarosz and Wiley, 2014; Van de Schoot et al., 2014; Wagenmakers et al., 2018).”