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Preprint: Practical Challenges and Methodological Flexibility in Prior Elicitation

This post is an extended synopsis of Stefan, A. M., Evans, N. J., & Wagenmakers, E.-J. (2019). Practical challenges and methodological flexibility in prior elicitation. Manuscript submitted for publication. Preprint available on PsyArXiv: https://psyarxiv.com/d42xb/

 
 
 

Abstract

It is a well-known fact that Bayesian analyses require the specification of a prior distribution, and that different priors can lead to different quantitative, or even qualitative, conclusions. Because the prior distribution can be so influential, one of the most frequently asked questions about the Bayesian statistical framework is: How should I specify the prior distributions? Here, we take a closer look at prior elicitation — a subjective Bayesian method for specifying (informed) prior distributions based on expert knowledge — and examine the practical challenges researchers may face when implementing this approach for specifying their prior distributions. Specifically, our review of the literature suggests that there is a high degree of methodological flexibility within current prior elicitation techniques. This means that the results of a prior elicitation effort are not solely determined by the expert’s knowledge, but also heavily depend on the methodological decisions a researcher makes in the prior elicitation process. Thus, it appears that prior elicitation does not completely solve the issue of prior specification, but instead shifts influential decisions to a different level. We demonstrate the potential variability resulting from different methodological choices within the prior elicitation process in several examples, and make recommendations for how the variability in prior elicitation can be managed in future prior elicitation efforts.
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A Breakdown of “Preregistration is Redundant, at Best”

In this sentence-by-sentence breakdown of the paper “Preregistration is Redundant, at Best”, I argue that preregistration is a pragmatic tool to combat biases that invalidate statistical inference. In a perfect world, strong theory sufficiently constrains the analysis process, and/or Bayesian robots can update beliefs based on fully reported data. In the real world, however, even astrophysicists require a firewall between the analyst and the data. Nevertheless, preregistration should not be glorified. Although I disagree with the title of the paper, I found myself agreeing with almost all of the authors’ main arguments.
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