On the white, the black, and the many shades of gray in between: Our reply to van Ravenzwaaij and Wagenmakers (2021)

In 2019 we wrote a paper (Tendeiro & Kiers, 2019) in Psychological Methods over null hypothesis Bayesian testing and its working horse, the Bayes factor. Recently, van Ravenzwaaij and Wagenmakers (2021) offered a response to our piece, also in this journal. Although we do welcome their contribution with thought-provoking remarks on our paper, we ended up concluding that there were too many ‘issues’ in van Ravenzwaaij and Wagenmakers (2021) that warrant a rebuttal. In this paper we both defend the main premises of our original paper and we put the contribution of van Ravenzwaaij and Wagenmakers (2021) under critical appraisal. Our hope is that this exchange between scholars decisively contributes towards a better understanding among psychologists of null hypothesis Bayesian testing in general and of the Bayes factor in particular.

Bayesian Testing for Exogenous Partition Structures in Stochastic Block Models

Network data often exhibit block structures characterized by clusters of nodes with similar patterns of edge formation. When such relational data are complemented by additional information on exogenous node partitions, these sources of knowledge are typically included in the model to supervise the cluster assignment mechanism or to improve inference on edge probabilities. Although these solutions are routinely implemented, there is a lack of formal approaches to test if a given external node partition is in line with the endogenous clustering structure encoding stochastic equivalence patterns among the nodes in the network. To fill this gap, we develop a formal Bayesian testing procedure which relies on the calculation of the Bayes factor between a stochastic block model with known grouping structure defined by the exogenous node partition and an infinite relational model that allows the endogenous clustering configurations to be unknown, random and fully revealed by the block–connectivity patterns in the network. A simple Markov chain Monte Carlo method for computing the Bayes factor and quantifying uncertainty in the endogenous groups is proposed. This strategy is evaluated in simulations, and in applications studying brain networks of Alzheimer’s patients.

Investigating the Ease-of-Retrieval Effect in an Eyewitness Context

The way in which individuals think about their own cognitive processes plays an important role in various domains. When eyewitnesses assess their confidence in identification decisions, they could be influenced by how easily relevant information comes to mind. This ease-of-retrieval effect has a robust influence on people’s cognitions in a variety of contexts (e.g., attitudes), but it has not yet been applied to eyewitness decisions. In three studies, we explored whether the ease with which eyewitnesses recall certain memorial information influenced their identification confidence assessments and related testimony-relevant judgements (e.g., perceived quality of view). We manipulated the number of reasons participants gave to justify their identification (Study 1; N = 343), and also the number of instances they provided of a weak or strong memory (Studies 2a & 2b; Ns = 350 & 312, respectively). Across the three studies, ease-of-retrieval did not affect eyewitnesses’ confidence or other testimony-relevant judgements. We then tried—and failed—to replicate Schwarz et al.’s (1991) original ease-of-retrieval finding (Study 3; N = 661). In three of the four studies, ease-of-retrieval had the expected effect on participants’ perceived task difficulty; however, frequentist and Bayesian testing showed no evidence for an effect on confidence or assertiveness ratings.