6 citations · 8 across the 5 of their papers we have counts for
6 papers
How accurate are Bayes factor-based null hypothesis tests? A simulation study
Daniel J. Schad, Martin Modrák
Bayes factor null hypothesis tests provide a viable alternative to frequentist measures of evidence quantification. Bayes factors for realistic data sets in areas like psychology c…
Data aggregation can lead to biased inferences in Bayesian linear mixed models and Bayesian ANOVA: A simulation study
Daniel J. Schad, Bruno Nicenboim, Shravan Vasishth
Bayesian linear mixed-effects models and Bayesian ANOVA are increasingly being used in the cognitive sciences to perform null hypothesis tests, where a null hypothesis that an effe…
Workflow Techniques for the Robust Use of Bayes Factors
Daniel J. Schad, Bruno Nicenboim, Paul-Christian Bürkner +2
Inferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide one general way to compare different hypotheses by their compatibility with the observed…
Toward a principled Bayesian workflow in cognitive science
Daniel J. Schad, Michael Betancourt, Shravan Vasishth
Experiments in research on memory, language, and in other areas of cognitive science are increasingly being analyzed using Bayesian methods. This has been facilitated by the develo…
The posterior probability of a null hypothesis given a statistically significant result
Daniel J. Schad, Shravan Vasishth
When researchers carry out a null hypothesis significance test, it is tempting to assume that a statistically significant result lowers Prob(H0), the probability of the null hypoth…
How to capitalize on a priori contrasts in linear (mixed) models: A tutorial
Daniel J. Schad, Shravan Vasishth, Sven Hohenstein +1
Factorial experiments in research on memory, language, and in other areas are often analyzed using analysis of variance (ANOVA). However, for effects with more than one numerator d…