## Pre-data insights update priors via Bayes’s theorem

D. R. Bickel, “Bayesian revision of a prior given prior-data conflict, expert opinion, or a similar insight: A large-deviation approach,” *Statistics* **52**, 552-570 (2018). Full text | 2015 preprint | Simple explanation

## How to adjust statistical inferences for the simplicity of distributions

D. R. Bickel, “Confidence intervals, significance values, maximum likelihood estimates, etc. sharpened into Occam’s razors,” Working Paper, University of Ottawa, <hal-01799519>** **https://hal.archives-ouvertes.fr/hal-01799519 (2018). 2018 preprint | Slides

## Should the default significance level be changed from 0.05 to 0.005?

My comments in this discussion of “Redefine statistical significance”:

The call for smaller significance levels cannot be based only on mathematical arguments that p values tend to be much lower than posterior probabilities, as Andrew Gelman and Christian Robert pointed out in their comment (“Revised evidence for statistical standards”).

In the rejoinder, Valen Johnson made it clear that the call is also based on empirical findings of non-reproducible research results. How many of those findings are significant at the 0.005 level? Should meta-analysis have a less stringent standard?

…

“Irreplicable results can’t possibly add empirical clout to the mathematical argument unless it is already known or assumed to be caused by a given cut-off, and further, that lowering it would diminish those problems.”

The preprint cites empirical results to support its use of the 1:10 prior odds. If that is in fact a reliable estimate of the prior odds for the reference class of previous studies, then, in the absence of other relevant information, it would be reasonable to use as input for Bayes’s theorem.

John Byrd asks, “Is 1:10 replicable?” Is it important to ask whether a 1:1 prior odds can be rejected at the 0.005 significance level?

END

## An R package to transform false discovery rates to posterior probability estimates

There are many estimators of false discovery rate. In this package we compute the Nonlocal False Discovery Rate (NFDR) and the estimators of local false discovery rate: Corrected False discovery Rate (CFDR), Re-ranked False Discovery rate (RFDR) and the blended estimator.

Source: CRAN – Package CorrectedFDR

## The Fiducialist Papers: Evidence and Likelihood

“The Fiducialist Papers” was just added to the name of the Evidence and Likelihood website.

## LFDR.MLE-package function | R Documentation

Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE):