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      Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP

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          Abstract

          Background

          Despite its popularity as an inferential framework, classical null hypothesis significance testing (NHST) has several restrictions. Bayesian analysis can be used to complement NHST, however, this approach has been underutilized largely due to a dearth of accessible software options. JASP is a recently developed open-source statistical package that facilitates both Bayesian and NHST analysis using a graphical interface. This article provides an applied introduction to Bayesian inference with Bayes factors using JASP.

          Methods

          We use JASP to compare and contrast Bayesian alternatives for several common classical null hypothesis significance tests: correlations, frequency distributions, t-tests, ANCOVAs, and ANOVAs. These examples are also used to illustrate the strengths and limitations of both NHST and Bayesian hypothesis testing.

          Results

          A comparison of NHST and Bayesian inferential frameworks demonstrates that Bayes factors can complement p-values by providing additional information for hypothesis testing. Namely, Bayes factors can quantify relative evidence for both alternative and null hypotheses. Moreover, the magnitude of this evidence can be presented as an easy-to-interpret odds ratio.

          Conclusions

          While Bayesian analysis is by no means a new method, this type of statistical inference has been largely inaccessible for most psychiatry researchers. JASP provides a straightforward means of performing reproducible Bayesian hypothesis tests using a graphical “point and click” environment that will be familiar to researchers conversant with other graphical statistical packages, such as SPSS.

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          Most cited references23

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          Bayesian inference for psychology. Part II: Example applications with JASP

          Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.
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            A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability.

            The statistical test of hypothesis of no difference between the average bioavailabilities of two drug formulations, usually supplemented by an assessment of what the power of the statistical test would have been if the true averages had been inequivalent, continues to be used in the statistical analysis of bioavailability/bioequivalence studies. In the present article, this Power Approach (which in practice usually consists of testing the hypothesis of no difference at level 0.05 and requiring an estimated power of 0.80) is compared to another statistical approach, the Two One-Sided Tests Procedure, which leads to the same conclusion as the approach proposed by Westlake based on the usual (shortest) 1-2 alpha confidence interval for the true average difference. It is found that for the specific choice of alpha = 0.05 as the nominal level of the one-sided tests, the two one-sided tests procedure has uniformly superior properties to the power approach in most cases. The only cases where the power approach has superior properties when the true averages are equivalent correspond to cases where the chance of concluding equivalence with the power approach when the true averages are not equivalent exceeds 0.05. With appropriate choice of the nominal level of significance of the one-sided tests, the two one-sided tests procedure always has uniformly superior properties to the power approach. The two one-sided tests procedure is compared to the procedure proposed by Hauck and Anderson.
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              The Abuse of Power

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                Author and article information

                Contributors
                +47 23 02 73 50 , daniel.quintana@medisin.uio.no
                drwwilliams@ucdavis.edu
                Journal
                BMC Psychiatry
                BMC Psychiatry
                BMC Psychiatry
                BioMed Central (London )
                1471-244X
                7 June 2018
                7 June 2018
                2018
                : 18
                : 178
                Affiliations
                [1 ]NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo, and Oslo University Hospital, Building 49, Oslo University Hospital, Ullevål, Kirkeveien 166, PO Box 4956, N- 0424 Nydalen, Oslo Norway
                [2 ]ISNI 0000 0004 1936 9684, GRID grid.27860.3b, Department of Psychology, , University of California, Davis, ; Davis, CA USA
                Author information
                http://orcid.org/0000-0003-2876-0004
                Article
                1761
                10.1186/s12888-018-1761-4
                5991426
                29879931
                53015443-7feb-42f0-b936-0f2fe1de158c
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 30 January 2018
                : 23 May 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004191, Novo Nordisk;
                Award ID: NNF16OC0019856
                Categories
                Technical Advance
                Custom metadata
                © The Author(s) 2018

                Clinical Psychology & Psychiatry
                statistics,bayesian analysis,research methods,p-values,null hypothesis significance testing,software

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