Memory Lane: August 6, 2013. My initial post on JSM13 (8/5/13) was here.
Nate Silver gave his ASA Presidential talk to a packed audience (with questions tweeted[i]). Here are some quick thoughts—based on scribbled notes (from last night). Silver gave a list of 10 points that went something like this (turns out there were 11):
1. statistics are not just numbers
2. context is needed to interpret data
3. correlation is not causation
4. averages are the most useful tool
5. human intuitions about numbers tend to be flawed and biased
6. people misunderstand probability
7. we should be explicit about our biases and (in this sense) should be Bayesian?
8. complexity is not the same as not understanding
9. being in the in crowd gets in the way of objectivity
10. making predictions improves accountability
Just to comment on #7, I don’t know if this is a brand new philosophy of Bayesianism, but his position went like this: Journalists and others are incredibly biased, they view data through their prior conceptions, wishes, goals, and interests, and you cannot expect them to be self-critical enough to be aware of, let alone be willing to expose, their propensity toward spin, prejudice, etc. Silver said the reason he favors the Bayesian philosophy (yes he used the words “philosophy” and “epistemology”) is that people should be explicit about disclosing their biases. I have three queries: (1) If we concur that people are so inclined to see the world through their tunnel vision, what evidence is there that they are able/willing to be explicit about their biases? (2) If priors are to be understood as the way to be explicit about one’s biases, shouldn’t they be kept separate from the data rather than combined with them? (3) I don’t think this is how Bayesians view Bayesianism or priors—is it? Subjective Bayesians, I thought, view priors as representing prior or background information about the statistical question of interest; but Silver sees them as admissions of prejudice, bias or what have you. As a confession of bias, I’d be all for it—though I think people may be better at exposing other’s biases than their own. Only thing: I’d need an entirely distinct account of warranted inference from data.
This does possibly explain some inexplicable remarks in Silver’s book to the effect that R.A. Fisher denied, excluded, or overlooked human biases since he disapproved of adding subjective prior beliefs to data in scientific contexts. Is Silver just about to recognize/appreciate the genius of Fisher (and others) in developing techniques consciously designed to find things out despite knowledge gaps, variability, and human biases? Or not?
Share your comments and/or links to other blogs discussing his talk (which will surely be posted if it isn’t already). Fill in gaps if you were there—I was far away… (See also my previous post blogging the JSM).
[i] What was the point of this, aside from permitting questions to be cherry picked? (It would have been fun to see ALL the queries tweeted.) The ones I heard were limited to: how can we make statistics more attractive, who is your favorite journalist, favorite baseball player, and so on. But I may have missed some, I left before the end.
Some reader comments on JSM 14 are here. Feel free to add comments here or there on either JSM.