We congratulate Drs. Jackson and White on a very interesting paper, providing a comprehensive summary of explicit and implicit normality and independence assumptions commonly being made in randomeffects meta-analysis that are easily overlooked and that may deserve more consideration . Some of the problems discussed, however, are less of an issue when analyses are done in a Bayesian framework. Normality assumptions are made explicit in the model's likelihood specification. Any departure from assumptions 1-4 (as listed in Tab. 3) then may cause problems, just as in the frequentist case. Additional assumptions enter the analysis in the form of the prior specification, expressing the information on parameters before taking the data into consideration. These can usually be made reasonably vague, if desired (e.g., a uniform prior for the effect µ, and a weakly informative prior for the heterogeneity τ ), and these may also be subjected to sensitivity analyses (Röver, 2017). At the inference or prediction stage, computations are usually carried out exactly, following Bayes' theorem, and, unlike in the frequentist framework, no approximations are necessary at this point (Spiegelhalter et al., 2004, Sec. 3). Normal or other approximations could of course also be used in a Bayesian analysis, e.g., when extrapolating around the posterior mode (Gelman et al., 2014, Sec. 13) or when when analysis is based on INLA (Rue et al., 2009). Such methods have been proposed for network meta-analysis (Sauter and Held, 2015;Günhan et al., 2018), however, such cases are usually explicitly indicated.To exemplify the issue, consider the smoking cessation example data (example one). We utilize the bayesmeta R package to derive the posterior distribution for the logarithmic odds ratio (log-OR), using an (improper) uniform prior for the effect µ and a half-normal prior with scale 0.5 for the heterogeneity τ (Röver, 2017). For the frequentist analyses, we use the metafor package with default settings (Viechtbauer, 2010). Figure 1 illustrates the normal approximation utilized in the construction of the confidence interval along with the effect's posterior density. The (marginal) posterior distribution is not normal, but rather a normal mixture (Röver, 2017), as becomes obvious when comparing to a normal approximation (based on matching mean and variance) that is also shown.Differences between (frequentist) normal approximation and posterior tend to be particularly large in case of substantial uncertainty in the heterogeneity parameter. While this happens especially in the common case of few studies (Friede et al., 2017a; see also Fig. 1), differences are still noticeable in the present data set consisting of twelve seemingly homogeneous OR estimates, where, due to an estimated heterogeneity of τ = 0, effectively one ends up performing a common-effect analysis in the frequentist framework. An value of τ = 0 is a "most optimistic" estimate here, in the sense that it will lead to the shortest possible confidence interval for a random-effects analy...