The Bayesian paradigm presents a fresh perspective on statistical
inference, providing pragmatic researchers with new opportunities, for
instance to design efficient experiments, to quantify statistical
evidence (either for the alternative hypothesis or for the null
hypothesis), to monitor that evidence as the data accumulate, to use
meaningful prior knowledge for strengthening the link between
psychological theory and statistical model, and to take into account
the model-selection uncertainty that inevitably arises whenever
multiple models are in play. These and other advantages will be
demonstrated with concrete applications featuring user-friendly
software packages such as JASP (jasp-stats.org) and Shiny.