## What’s the goal of statistics in scientific applications?

the goal [of statistical inference in science] is not to infer highly probable claims (in the formal sense)* but claims which have been highly probed and have passed severe probes

Source: Deborah G. Mayo’s Performance or Probativeness? E.S. Pearson’s Statistical Philosophy | Error Statistics Philosophy

## “a list of possibly predatory publishers” based on Beall’s List

This is a list of possibly predatory publishers. The kernel for this list was extracted from the archive of Beall’s List at web.archive.org. It will be updated as new information or suggested edits are submitted or found by the maintainers of this site.

Source: List of Predatory Publishers | Stop Predatory Journals (accessed 24 August 2017)

## Statistics & Biostatistics Master’s Studentships in Ottawa

Reliable interpretation of genomic information makes unprecedented demands for innovations in statistical methodology and its application to biological systems. This unique opportunity drives research at the Evidence and Likelihood Lab of the Ottawa Institute of Systems Biology (http://www.davidbickel.com). David Bickel seeks new graduate students who will conduct original research involving the creation and evaluation of novel statistical tools for application to the analysis of transcriptomics, proteomics, metabolomics, and/or genome-wide-association data.

Each student will work toward an MSc degree in the Mathematics and Statistics Program at the University of Ottawa. MSc students have the additional option of choosing a Bioinformatics or Biostatistics Specialization. Financial support is available.

Intellectual curiosity and high mathematical aptitude are essential, as is the ability to quickly code and debug computer programs. Strong self motivation and good communication skills are also absolutely necessary. The following qualities are desirable but not required: coursework in bioinformatics, computer science, numerical methods, numerical analysis, software engineering, statistics, and/or biology; familiarly with R, S-PLUS, Stan, JAGS, Mathematica, C, Fortran, and/or LaTeX; experience with UNIX or Linux.

Canadians (by citizenship or permanent residency) are especially encouraged to apply, as are all exceptional students. To be considered, send a PDF CV that has your GPA and contact information of two references to dbickel@uOttawa.ca with a cover letter in the body of the message. Please indicate in the subject line of the message your immigration status (“Canadian citizen,” “Canadian PR,” or “visa”) and, optionally, a specialization (“Bioinformatics” or “Biostatistics”). Only those selected for further consideration will receive a response.

## “Can You Change Your Bayesian Prior?”

Sometimes. A subjective Bayesian encountering completely unexpected data changes the prior:In the philosophy literature, that has been compared to changing the premises of a deductive argument. It has been argued that just as one may revise a premise without abandoning deductive logic as a tool, one may revise a prior without abandoning Bayesian updating as a tool.

## SSC 2017 talk on the misleading nature of false discovery rates

Planned for today’s SSC 2017 session Statistical Methods for Omics Data (Room E3 270):

“Correcting false discovery rates for their bias toward false positives”

## The generalized fiducial distribution: A kinder, more objective posterior?

**MR3561954**

Generalized fiducial inference: a review and new results. (English summary)

*J. Amer. Statist. Assoc.*111 (2016), no. 515, 1346–1361.

62A01 (62F99 62G05 62J05)

Other approaches to fiducial inference bring subjectivity more to the forefront. For example, G. N. Wilkinson had highlighted the incoherence of fiducial distributions formulated in a more Fisherian flavor [J. Roy. Statist. Soc. Ser. B 39 (1977), no. 2, 119–171; MR0652326]. More recently, R. J. Bowater [AStA Adv. Stat. Anal. 101 (2017), no. 2, 177–197] endorsed an explicitly subjective interpretation of fiducial probability. For the place of generalized fiducial inference in the context of other fiducial approaches, see [D. L. Sonderegger and J. Hannig, in Contemporary developments in statistical theory, 155–189, Springer Proc. Math. Stat., 68, Springer, Cham, 2014; MR3149921] and the papers it {MR3149921} cites.

- A weak-limit definition of a generalized fiducial distribution.
- Sufficient conditions for a generalized fiducial distribution to have asymptotic frequentist coverage.
- Novel formulas for computing a generalized fiducial distribution and a fiducial probability of a model.

The fiducial probability of a model is applicable to both model selection and model averaging. A seemingly different fiducial method of averaging statistical models was independently proposed by D. R. Bickel [“A note on fiducial model averaging as an alternative to checking Bayesian and frequentist models”, preprint, Fac. Sci. Math. Stat., Univ. Ottawa, 2015].

Reviewed by David R. Bickel