RMS Causal Inference

[R_cubed]

@Vattaka

I chose a Bayesian approach on top of that, but I ran into skepticism of the results.

It might be worth exploring IJ Good’s Reverse Bayes approach, that Robert Matthews and Leonhart Held have described as Bayesian Analysis of Credibility. I posted a few re-analyses of studies (that reported frequentist intervals) to derive the Bayesian prior that rendered the report credible with the null, or with the existence of an effect.

Frequentist vs Bayesian debate applied to real-world data analysis: any philosophical insights?

I think we need to keep in mind the important distinction between correct frequentist statistics, and simply bad statistics. @Sander posted a link to a youtube video (I’ve also posted it here in a few different threads) on p-values, confidence intervals, and their common, but incorrect interpretations. [Digging Deeper Into p-values: NISS Webinar Follow-up with Three Authors - November, 2020] It was mentioned in passing in the video that Jeffries and Fisher would pose statistical p…

This paper mentioned by @AndrewPGrieve is also worth study

The most compatible value with the data within a confidence interval

R_cubed I agree that we need to give serious thought to how we report the results of our studies. It is more than a little self-centred, particularly we Bayesians that everyone will be content, or accept, with “Our posterior”. The American econometrician Clifford Hildreth wrote a little known paper in the early 1960s enititled “Bayesian statisticians and remote clients” (Bayesian Statisticians and Remote Clients on JSTOR) in which he considered what potential parcels of information we might …