BackgroundDespite 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.MethodsWe 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.ResultsA 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.ConclusionsWhile 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.

In response to recommendations to redefine statistical significance to p ≤ .005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.

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An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through covariances in the precision matrix. While the graphical lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this paper, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted (p ≪ n). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce non-regularized methods based on multiple regression and a non-parametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the non-regularized methods can be used to reduce the false positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The non-regularized methods have been implemented in the R package GGMnonreg.

icant by control experiments. Increasing the p-nitrophenol concentration sevenfold did not cause a decrease in observed proton release, nor did the substitution of indole for the substrate. We have previously shown that between pH 6.1 and 6.4 the fraction of a-chymotrypsin in the active conformation is pH independent at 83-84% so that the substrate-induced perturbation of the conformational equilibrium does not lead to pH changes.6 This is consistent with case c, and rules out a and b, as previously postulated from indirect evidence.1 An additional intermediate occurs after the Michaelis complex and does not accumulate.By analogy with nonenzymatic acyl transfer reactions7 a reasonable interpretation1 is that this intermediate is the tetrahedral adduct of Ser-195 with the carboxyl group of the amide. At low pH there is rate-determining formation of the intermediate, as this partitions favorably toward products by an acid-catalyzed route. At high pH its breakdown is rate determining; the intermediate reverts faster to the Michaelis complex than to the acyl-enzyme (see Figure 2).