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Theoretical statistics is the theory of applied statistics: how to think about what we do (My talk at the University of Michigan this Friday 3pm)

Theoretical statistics is the theory of applied statistics: how to think about what we do

Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University

Working scientists and engineers commonly feel that philosophy is a waste of time. But theoretical and philosophical principles can guide practice, so it makes sense for us to keep our philosophical foundations up to date. Much of the history of statistics can be interpreted as a series of expansions and inclusions: formalizations of procedures and ideas which had been previously considered outside the bounds of formal statistics. In this talk we discuss several such episodes, including the successful (in my view) incorporations of hierarchical modeling and statistical graphics into Bayesian data analysis, and the bad ideas (in my view) of null hypothesis significance testing and attempts to compute the posterior probability of a model being true. I’ll discuss my own philosophy of statistics and also the holes in my current philosophical framework.

It’s happening at 3pm Friday 10 Feb, in Michigan League – Kuenzel Room, and it’s the Foundations of Belief & Decision Making Lecture, organized by the Philosophy Department.

13 Comments

  1. Geoffrey says:

    Will there be a live stream, or a recording posted to youtube by any chance?

  2. Rahul says:

    Isn’t “philosophical” an entirely different can of worms than mere theoretical?

  3. Eric says:

    Andrew:

    > Hey, once they start putting my talks on Youtube, I won’t be able to recycle my jokes. Can you imagine—having to come up with new material for each public appearance? It would kill me.

    But seriously, folks, this looks important and I don’t want to miss it because I’m not at the right university to hear you speak in person. I really want to hear what you have to say about this in a more formal environment than blog snippets (intriguing though they are — really).

    This is a real issue for people located far from many of the leading research institutions.

  4. Tom says:

    “including the successful (in my view) incorporations of hierarchical modeling and statistical graphics into Bayesian data analysis”

    I’m interested in current best-practices in statistical graphics. I saw Journal of Computational and Graphical Statistics in your publication list, but that 2012 paper suggested graphics still haven’t received the attention they deserve. Can you recommend a book that synthesizes advancements in statistical graphics?

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