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Decision makers often combine multiple pieces of information to make performance predictions and hiring decisions. More valid predictions are made when information is combined algorithmically (mechanical prediction) rather than in the decision-maker's mind (holistic prediction). Yet, decision makers rarely use algorithms in practice. One reason is that decision makers are worried about negative evaluations from other stakeholders such as colleagues when using algorithms. We hypothesized that such stakeholders evaluate decision makers more positively when they use autonomy-enhancing algorithmic procedures (AEAPs, holistically adjust predictions from a prescribed algorithm or self-design an algorithm), than when they use a prescribed algorithm. Relatedly, we hypothesized that decision makers who use AEAPs are less worried about negative stakeholder evaluations, and more likely to use algorithms in performance predictions. In Study 1 (N = 582), stakeholders evaluated decision makers more positively when they used AEAPs rather than a prescribed algorithm. In Study 2 (N = 269), decision makers were less worried about negative stakeholder evaluations and more likely to use AEAPs compared to a prescribed algorithm. Importantly, using AEAPs also resulted in substantially higher predictive validity than holistic prediction. We recommend the use of self-designed algorithms to improve perceptions and validity. [ABSTRACT FROM AUTHOR]

Linde et al. (2021) compared the "two one-sided tests" the "highest density interval—region of practical equivalence", and the "interval Bayes factor" approaches to establishing equivalence in terms of power and Type I error rate using typical decision thresholds. They found that the interval Bayes factor approach exhibited a higher power but also a higher Type I error rate than the other approaches. In response, Campbell and Gustafson (2022) showed that the performances of the three approaches can approximate one another when they are calibrated to have the same Type I error rate. In this article, we argue that these results have little bearing on how these approaches are used in practice; a concrete example is used to highlight this important point. Linde et al. (2021) compared three statistical procedures for establishing that two groups are equivalent on some kind of measure in terms of how often they conclude equivalence even though the groups are truly nonequivalent and how often they conclude equivalence when the groups are truly equivalent. They found that the "interval Bayes factor" procedure correctly and incorrectly concludes equivalence more often than the "two one-sided tests" and the "highest density interval - region of practical equivalence" procedures, when using typical decision thresholds. In response, Campbell and Gustafson (2022) showed that the three procedures could make the same amount of correct and incorrect equivalence decisions by adjusting the decision thresholds. In this article, we argue that the findings of Campbell and Gustafson (2022) have little bearing on statistical practice and we use a concrete example to illustrate this point. [ABSTRACT FROM AUTHOR]

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 the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The article is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines. 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 Bayes factor provides a method for quantifying the relative evidence for two competing hypotheses that are both instantiated by specific statistical models with prior distributions on the parameters. This general approach can be used to address many specific, theoretically relevant research questions. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: whether a randomized experiment has an effect or not (point null hypothesis), whether an effect is inside or outside a range of negligible effect sizes (interval hypothesis), whether a set of means follows a specific order (informative hypothesis), whether a set of studies jointly corroborate a theoretical claim (evidence synthesis), which variables are most relevant for prediction (variable selection), and which model provides the best account of latent processes (cognitive modeling). We elaborate what each application entails, give illustrative examples with reproducible files for the software R and JASP, and provide an overview of key references and software with links to other applications. We concluded with a discussion of the opportunities and pitfalls of the Bayes factor. [ABSTRACT FROM AUTHOR]