The p-value quantifies the discrepancy between the data and a null hypothesis of interest,
usually the assumption of no difference or no effect. A Bayesian approach allows the …

Well-designed experiments are likely to yield compelling evidence with efficient sample
sizes. Bayes Factor Design Analysis (BFDA) is a recently developed methodology that …

Regressions and meta-regressions are widely used to estimate patterns and effect sizes in
various disciplines. However, many biological and medical analyses use relatively low …

The method of model averaging has become an important tool to deal with model
uncertainty, for example in situations where a large amount of different theories exist, as are …

Supplement to “Penalising Model Component Complexity: A Principled, Practical Approach
to Constructing Priors”. The supplementary material contains the proofs of all theorems …

A core aspect of science is using data to assess the degree to which data provide evidence
for competing claims, hypotheses, or theories. Evidence is by definition something that …

Psychological theories are statements of constraint. The role of hypothesis testing in
psychology is to test whether specific theoretical constraints hold in data. Bayesian statistics …

We provide a review of prior distributions for objective Bayesian analysis. We start by
examining some foundational issues and then organize our exposition into priors for: i) …

This paper considers inference of causal structure in a class of graphical models called
conditional DAGs. These are directed acyclic graph (DAG) models with two kinds of …

Null hypothesis significance testing (NHST) has been under scrutiny for decades. The
literature shows overwhelming evidence of a large range of problems affecting NHST. One …