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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.
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