From theories to models to predictions: A Bayesian model comparison approach

A key goal in research is to use data to assess competing hypotheses or theories. An alternative to the conventional significance testing is Bayesian model comparison. The main idea is that competing theories are represented by statistical models. In the Bayesian framework, these models then yield predictions about data even before the data are seen. How well the data match the predictions under competing models may be calculated, and the ratio of these matches - the Bayes factor - is used to assess the evidence for one model compared to another. We illustrate the process of going from theories to models and to predictions in the context of two hypothetical examples about how exposure to media affects attitudes toward refugees.