As the limitations of p-values have been discussed widely, only three important problems are listed here, which are especially harmful in medical decision making and research: (1) it is known that p-values are prone to overestimating effects (2) they inevitably state effects if none exist with a fixed probability (3) they are prone to false interpretation by researchers. The ongoing use of NHST also indicates that the p-value as a measure of significance is still widely accepted despite its drawbacks and stays resilient to the repeated criticism. show that most problems of NHST have not been solved by now. The misuse and abuse of p-values in particular in medical research have been criticised in countless venues, and the official American Statistical Association (ASA) statement in 20 by Wasserstein and Lazar and Wasserstein et al. While the agreed standard in medical decision making, in the last few years more and more problems inherent in NHST have been revealed. The well known p-value states exactly the probability of observing a result as extreme as the one observed or even more extreme when the null hypothesis H 0 were true. If the observed value of the test statistic passes a given threshold, which is located in the tails of F, then the null hypothesis H 0 is rejected, because observing such a value would be quite unplausible if H 0 were true. The centerpiece of frequentist inference is a test statistic T, which can be computed from the raw data, and which is known to have a specific distribution F under the null hypothesis H 0. The results of medical research therefore stand on the shoulders of the frequentist statistical philosophy, which roots back to the early days of Fisher and Neyman-Pearson. Null hypothesis significance testing (NHST) remains the dominating inferential approach in medical research. Through an easy point-and-click interface researchers used to other graphical statistical packages like SPSS can seemlessly transition to JASP and benefit from the listed advantages with only few limitations. Medical decision making can be complemented by Bayesian hypothesis testing in JASP, providing richer information than single p-values and thus strengthening the credibility of an analysis. ConclusionsД«ayesian inference has not been widely used by now due to the dearth of accessible software. Also, effect sizes can be precisely estimated in the Bayesian paradigm via JASP. While p-values can only reject the null hypothesis, the Bayes factor can state evidence for both the null and the alternative hypothesis, making confirmation of hypotheses possible.

Specifically, Bayesian hypothesis testing via Bayes factors can complement and even replace NHST in most situations in JASP.

The comparison shows the strengths and limitations of JASP for frequentist NHST and Bayesian inference.

This article provides a non-technical introduction to Bayesian hypothesis testing in JASP by comparing traditional tests and statistical methods with their Bayesian counterparts. JASP is an open-source software for common operating systems, which has recently been developed to make Bayesian inference more accessible to researchers, including the most common tests, an intuitive graphical user interface and publication-ready output plots. Bayesian methods can complement or even replace frequentist NHST, but these methods have been underutilised mainly due to a lack of easy-to-use software. Although null hypothesis significance testing (NHST) is the agreed gold standard in medical decision making and the most widespread inferential framework used in medical research, it has several drawbacks.

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