SCS Reads 2019-2020

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  • A topic need not occupy the entire academic year and we could plan to consider more than one topic.
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New candidates topics for 2019-2020

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Candidates from 2018-2019

  • Reproducibility of research: a crisis in Statistics??: 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.
    • 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
    • We can use articles in 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.
  • 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]

Links to past episodes of SCS Reads

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