Publication detail

Hypothesis testing is a special form of model selection. Once a pair of competing models is fully defined, their definition immediately leads to a measure of how strongly each model supports the data. The ratio of their support is often called the likelihood ratio or the Bayes factor. Critical in the model-selection endeavor is the specification of the models. In the case of hypothesis testing, it is of the greatest importance that the researcher specify exactly what is meant by a "null" hypothesis as well as the alternative to which it is contrasted, and that these are suitable instantiations of theoretical positions. Here, we provide an overview of different instantiations of null and alternative hypotheses that can be useful in practice, but in all cases the inferential procedure is based on the same underlying method of likelihood comparison. An associated app can be found at https://osf.io/mvp53/. This article is the work of the authors and is reformatted from the original, which was published under a CC-By Attribution 4.0 International license and is available at https://psyarxiv.com/wmf3r/.

@article{etz_etal:2018:hypothesis,
    title   = {{B}ayesian inference and testing any hypothesis you can specify},
    author  = {Etz, Alexander and Haaf, Julia and Rouder, Jeffrey and Vandekerckhove, Joachim},
    year    = {2018},
    journal = {Advances in Methods and Practices in Psychological Science},
    volume  = {1},
    pages   = {281--295}
}