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In keeping with these neurobiologic premises and the evident assumption that every human thought, hypothesis and theory must first exist as a specific connection between neurons, is a Bayesian statistical …

that inference from the non-Bayesian approaches might be optimistic. … Finally, Bayesian analysis forces you to explicitly acknowledge your assumptions. Anyway, I would caution against a meta-analysis in which Bayesian and frequentist approaches are quite conflicting. …

In keeping with these theoretical premises, which statistical framework is more appropriate, the frequentist or the Bayesian? …

As very rarely are effects this large, is this prior justifiable? I think your priors are OK, as long as you can defend them with extra-statistical arguments (e.g by looking at established works …

My choice would be to continue using p values, but simply adding confidence/credible intervals, and possibly for the primary outcomes prediction intervals. There is a very nice book by Douglas Altman …

Should I use upper case (capital) $P$ or lower case $p$ to denote $P$/$p$-values and Bayesian probabilities? "p" for both frequentist and Bayesian analyses? … "P" for both frequentist and Bayesian analyses? "p" for frequentist analyses and "P" for Bayesian analyses? or "P" for frequentist analyses and "p" for Bayesian analyses? …

A colleague of mine wishes to conduct a network meta-analysis of correlation coefficients for a set of imaging tests (eg ultrasound, computed tomography, magnetic resonance imaging, and so forth). H …

Before beginning, though, I have a particular question with very general implications: Should we begin teaching statistics using a Bayesian or frequentist framework? … Researching around I have seen that a common approach is beginning with a brief introduction on frequentist statistics followed by an in depth discussion of Bayesian statistics (e.g. Stangl). …