Abstract
BackgroundAlthough 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. 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. 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. This article provides a non-technical introduction to Bayesian hypothesis testing in JASP by comparing traditional tests and statistical methods with their Bayesian counterparts.ResultsThe comparison shows the strengths and limitations of JASP for frequentist NHST and Bayesian inference. Specifically, Bayesian hypothesis testing via Bayes factors can complement and even replace NHST in most situations in 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. Also, effect sizes can be precisely estimated in the Bayesian paradigm via JASP.ConclusionsBayesian inference has not been widely used by now due to the dearth of accessible software. 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. 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.
Highlights
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
The analyses themselves are written in either R or C++ to Results To study the behaviour of Bayesian methods in Jeffreys’ awesome statistics package (JASP), three typical questions arising in medical research are used as a scaffold: (1) Do multiple groups differ on an observed metric variable, and if so, how large is the effect size? (2) Do two groups differ on an observed metric variable, and if so, how large is the effect size between both groups? (3) How strong is the relationship between two observed variables? Usually null hypothesis significance testing (NHST) in form of (1) an analysis of variance
It will be shown that Bayesian versions of these statistical procedures can complement NHST and provide even richer information for medical research
Summary
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. 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 [13]; (2) they inevitably state effects if none exist with a fixed probability [14]; (3) they are prone to false interpretation by researchers [15]. This problem is in particular problematic in clinical decision making with possibly devastating consequences for patients and the progress of medical science, see Ioannidis [9, 16]. Point (2) is crucial, as for medical science but in much more generality, McElreath and Smaldino [17] stress that “the most important factors in improving the reliability of research are the rate of false positives”
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