  We provide an introductory review of Bayesian data analytical methods, with a
focus on applications for linguistics, psychology, psycholinguistics, and
cognitive science. The empirically oriented researcher will benefit from making
Bayesian methods part of their statistical toolkit due to the many advantages
of this framework, among them easier interpretation of results relative to
research hypotheses, and flexible model specification. We present an informal
introduction to the foundational ideas behind Bayesian data analysis, using, as
an example, a linear mixed models analysis of data from a typical
psycholinguistics experiment. We discuss hypothesis testing using the Bayes
factor, and model selection using cross-validation. We close with some examples
illustrating the flexibility of model specification in the Bayesian framework.
Suggestions for further reading are also provided.
