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Description: One of the most common applications of Bayes’ theorem for inferential purposes consists of computing the ratio between the probability of the alternative and the null hypotheses via a Bayes factor, which allows quantifying the most likely explanation of the data between the two. However, the actual scientific questions that researchers are interested in are rarely well represented by the classically defined alternative and null hypotheses. Bayesian informative hypothesis testing offers a valid and easy way to overcome such limitations via a model selection procedure that allows comparing highly specific hypotheses formulated in terms of equality (A = B) or inequality (A > B) constraints amongst parameters. Although packages for testing informative hypotheses in the most used statistical software have been developed in recent years, they are still rarely used, possibly because their implementation and interpretation may not be straightforward. Starting from a brief theoretical overview of the Bayesian theorem and its applications in statistical inference (namely, Bayes factor and Bayesian testing of informative hypotheses), this paper provides two step-by-step tutorials illustrating how to test, interpret, and report in a scientific paper Bayesian informative hypotheses testing using JASP and R/RStudio software for running a 2x2 Anova and a multiple linear regression. The complete JASP files, R code, and datasets used in the paper are freely available on the OSF page https://osf.io/dez9b/.