Most neuroscientists would agree that for brain research to progress, we have to know which
experimental manipulations have no effect as much as we must identify those that do have …

Previous surveys of the literature have shown that reports of statistical analyses often lack
important information, causing lack of transparency and failure of reproducibility. Editors and …

Despite the increasing popularity of Bayesian inference in empirical research, few practical
guidelines provide detailed recommendations for how to apply Bayesian procedures and …

A worrying number of psychological findings are not replicable. Diagnoses of the causes of
this “replication crisis,” and recommendations to address it, have nearly exclusively focused …

Statistical procedures such as Bayes factor model selection and Bayesian model averaging
require the computation of normalizing constants (eg, marginal likelihoods). These …

We introduce the fundamental tenets of Bayesian inference, which derive from two basic
laws of probability theory. We cover the interpretation of probabilities, discrete and …

Parameter estimation in evidence-accumulation models of choice response times is
demanding of both the data and the user. We outline how to fit evidence-accumulation …

To get evidence for or against a theory relative to the null hypothesis, one needs to know
what the theory predicts. The amount of evidence can then be quantified by a Bayes factor …

Specification of the prior distribution for a Bayesian model is a central part of the Bayesian
workflow for data analysis, but it is often difficult even for statistical experts. In principle, prior …

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for
hypothesis evaluation and model selection. The present review highlights the potential of …