SCS Reads 2019-2020

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Links to past episodes of SCS Reads

Candidates for 2019-2020

  • __The Crisis in Statistics??__ Use the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-value and the concept of statistical significance, as a springboard to select articles from the special issue or elsewhere to discuss whether there is a crisis in statistics and what should we do.
  • Add other suggestions here or send them to

Initial discussion of topics

A view was expressed that we should consider this seminar series more broadly, with a view to:

  • wider, and more active participation by the seminar members
  • topics or book chapters that would:
    • enlist a volunteer as discussion leader, or organize the topic
    • perhaps involve some concrete, practical examples used to illustrate the topic

Without prejudice to the choice of The Book of Why on causal inference, some suggested topics mentioned are listed below, though the general view was that, if we took this route, only 3-4 should be considered for this year. I'm just listing these, but they deserve to be fleshed out more for us to consider how & whether they would work.

  • Reproducibility of research: How should statistical practice be informed by current controversies about the replication crisis and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. [MF: I should add some references here.]
    • Roger Peng, Reproducible Research in Computational Science, Science, Vol. 334, Issue 6060, pp. 1226-1227 [1]
    • RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter
  • Big Data problems: Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]
  • Some statistical methods topics, not yet clearly articulated:
    • Clustering methods [RC: Could this fall under the Big Data label?]
    • Robustness
    • Mediation
    • Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]
  • Consulting issues: Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]
  • Survey Sampling: Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada.
  • Evidence-based medicine: Ideas and implications
  • Machine Learning, AI, Deep Learning: An Overview
  • Disseminating technical information to non-technical audiences: in consulting and in teaching. [Could this be put together with 'consulting issues'?]
  • Missing Data
  • Statistical Paradoxes and Fallacies [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]

September 21

Talk: Michael Friendly, 100+ Years of Titanic Graphs

October 5

October 19

  • Chapters 2 and 3 of the Book of Why

November 2

  • Chapters 4 and 5 of the Book of Why


January 4

February 1

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