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Even though most studies rely on a frequentist interpretation of statistical readouts, the application of Bayesian statistics has increased due to the availability of easy-to-use software suites and an increased outreach favouring this topic in the scientific community. Bayesian statistics take our prior knowledge together with the obtained data to express a degree of belief how likely a certain event is. Bayes factor hypothesis testing (BFHT) provides a straightforward method to evaluate multiple hypotheses at the same time and provides evidence that favors the null hypothesis or alternative hypothesis. In the present perspective, we show the merits of BFHT for three different use cases, including a clinical trial, basic research as well as a single case study. 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