Within any education category, richer people vote more Republican. In contrast, the pattern of education and voting is nonlinear. High school graduates are more Republican than non-HS grads, but after that, the groups with more education tend to vote more Democratic. At the very highest education level tabulated in the survey, voters with post-graduate degrees lean toward the Democrats. Except for the rich post-graduates; they are split 50-50 between the parties.

What does this say about America’s elites? If you define elites as high-income non-Hispanic whites, the elites vote strongly Republican. If you define elites as college-educated high-income whites, they vote moderately Republican.

There is no plausible way based on these data in which elites can be considered a Democratic voting bloc. To create a group of strongly Democratic-leaning elite whites using these graphs, you would need to consider only postgraduates (no simple college grads included, even if they have achieved social and financial success), and you have to go down to the below-$75,000 level of family income, which hardly seems like the American elites to me.

Andrew Gelman, Statistical Modeling... http://andrewgelman.com/2012/03/voting-patterns-of-americas-whites-from-the-masses-to-the-elites/

Andrew Gelman draws attention to a terrific paper by Mark Chaves on the religious congruence fallacy.

Religious congruence refers to consistency among an individual's religious beliefs and attitudes, consistency between religious ideas and behavior, and religious ideas, identities, or schemas that are chronically salient and accessible to individuals across contexts and situations. Decades of anthropological, sociological, and psychological research establish that religious congruence is rare, but much thinking about religion presumes that it is common. The religious congruence fallacy [emphasis added] occurs when interpretations or explanations unjustifiably presume religious congruence.

black swans

Andrew Gelman revisits his review of Taleb's The Black Swan.

And then there are parts of the review that make me really uncomfortable. As noted in the above quote, I was using the much-derided "picking pennies in front of a steamroller" investment strategy myself--and I knew it! Here's some more, again from 2007:

I'm only a statistician from 9 to 5

I try (and mostly succeed, I think) to have some unity in my professional life, developing theory that is relevant to my applied work. I have to admit, however, that after hours I'm like every other citizen. I trust my doctor and dentist completely, and I'll invest my money wherever the conventional wisdom tells me to (just like the people whom Taleb disparages on page 290 of his book).

Not long after, there was a stock market crash and I lost half my money. OK, maybe it was only 40%. Still, what was I thinking--I read Taleb's book and still didn't get the point!

Actually, there was a day in 2007 or 2008 when I had the plan to shift my money to a safer place. I recall going on the computer to access my investment account but I couldn't remember the password, was too busy to call and get it, and then forgot about it. A few weeks later the market crashed.

If only I'd followed through that day. Oooohhh, I'd be so smug right now. I'd be going around saying, yeah, I'm a statistician, I read Taleb's book and I thought it through, blah blah blah. All in all, it was probably better for me to just lose the money and maintain a healthy humility about my investment expertise.

Andrew was kind enough to have me to dinner (along with Jenny Davidson) while I was in New York; Andrew is probably one of the few who are more charismatic in person than in avatar (possibly because backed up by the exceptionally charismatic Caroline, Jakey and Zach). This in itself would be sufficient justification for blogging (at a purely personal level); the thing that is of real significance, though, is the fact that AG was able to write a review with self-determined word-count -- and then revisit it in light of events. Show me the paper publication that lets reviewers write a review of the review years later, at a word count dictated by developments in the world rather than by paper constraints-- I don't think so.

Granger causality is a standard statistical technique for determining whether one time series is useful in forecasting another. It is important to bear in mind that the term causality is used in a statistical sense, and not in a philosophical one of structural causation. More precisely a variable A is said to Granger cause B if knowing the time paths of B and A together improve the forecast of B based on its own time path, thus providing a measure of incremental predictability. In our case the time series of interest are market measures of returns, implied volatility, and realized volatility, or variable B. . . . Simply put, Granger's test asks the question: Can past values of trader positions be used to predict either market returns or volatility?

A report on Granger causality, discussed by Andrew Gelman at Statistical Modeling...

AG:

I have nothing to say on the particulars, as I have no particular expertise in this area. But in general, I'd prefer if researchers in this sort of problem were to try to estimate the effects of interest (for example, the amount of additional information present in some forecast) rather than setting up a series of hypothesis tests. The trouble with tests is that when they reject, it often tells us nothing more than that the sample size is large. And when they fail to reject, if often tells us nothing more than that the sample size is small. In neither case is the test anything like a direct response to the substantive question of interest.

My other Carter story comes from a conversation I had a couple years ago with an economist who's about my age, a man who said that one reason he and his family moved from town A to town B in his metropolitan area was that, in town B, they didn't feel like they were the only Republicans on their block.

Anyway, this guy described himself as a "Jimmy Carter Republican."

Me: You mean you liked Carter's policies on deregulation?

Him: No. I mean that Jimmy Carter made me a Republican.

Andrew Gelman at Statistical Modeling

Andrew Gelman on statistical fallacies in baseball reportage.

After being awarded a Rosenwald Fellowship, established by the clothing magnate Julius Rosenwald to aid black scholars, he attended the Institute for Advanced Study at Princeton but left after a year when, because of his race, he was not issued the customary invitation to become an honorary faculty member. At Berkeley, where the statistician Jerzy Neyman wanted to hire him in the mathematics department, racial objections also blocked his appointment.

Instead, Mr. Blackwell sent out applications to 104 black colleges on the assumption that no other schools would hire him. After working for a year at the Office of Price Administration, he taught briefly at Southern University in Baton Rouge, La., and Clark College in Atlanta before joining the mathematics department at Howard University in Washington in 1944.

Obituary of David Blackwell, NYT, ht Andrew Gelman

I think some of the confusion that has arisen from Ed Tufte's work is that people read his book and then want to go make cool graphs of their own. But cool like Amis, not cool like Orwell. We each have our own styles, and I'm not trying to tell you what to do, just to help you look at your own writing and graphics so you can think harder about what you want your style to be.

Andrew Gelman at Statistical Modeling...

Britain Britain Britain

Andrew Gelman has a post in which he links to an old post of mine on Seth Godin. Andrew took me to endorse the position of Seth Godin. (Divided by a common language, Bill, divided by a common language.)

I went back to look at my old post. What I said was that Seth Godin had a fresh perspective.

Um.

Andrew.

Um.

Oh Lord.

When I say that Seth Godin has a fresh perspective on an issue, what I mean is something like, Seth Godin is a monster raving loony who has boldly gone where no man has gone before; you and I, not being paid-up members of the Monster Raving Loony Party, had never in our wildest dreams imagined that anyone could put this forward as a serious position, we can now feel that we are not alone in the world because standing shoulder to shoulder in gobsmacked amazement.

Yes. All this conveyed in the simple phrase 'fresh perspective'.

Britain. Britain. Britain. You have destroyed my career, Britain, but I am unable not to love you.

Andrew Gelman on Christian Robert on classics of statistics.

Expressing the problem using a population distribution rather than a probability distribution has an additional advantage: it forces us to be explicit about the data-generating process.

Consider the disease-test example. The key assumption is that everybody (or, equivalently, a random sample of people) are tested. Or, to put it another way, we're assuming that the 10% base rate applies to the population of people who get tested. If, for example, you get tested only if you think it's likely you have the disease, then the above simplified model won't work.

This condition is a bit hidden in the probability model, but it jumps out (at least, to me) in the "population distribution" formulation. The key phrases above: "Of the 10 with the disease . . . Of the 90 without the disease . . . " We're explicitly assuming that all 100 people will get tested.

Andrew Gelman on assumptions underlying calculations of conditional probability.

damn lies and good graphs

maybe they're right that crappy chartjunk graphs are better than crappy non-chartjunk graphs. But I don't think it's appropriate to generalize to the claim that chartjunk graphs are better than good graphs.

Is chartjunk undeservedly maligned? Possibly, said a recent post on Infosthetics. Andrew Gelman offers the critique I wish I'd written (instead of, shame, shame, frivolously linking without comment). The rest here.

Gordon Lish in the House of Bayes

Andrew Gelman gives a statistician's take on the Lish method:

In any case, in the grand tradition of reviewing the review, I have some thoughts inspired by DeWitt, who quotes from this interview:

LRS: I was studying writing at college and then this professor showed up, a disciple of Gordon Lish, and we operated according to the Lish method. You start reading your work and then as soon as you hit a false note she made you stop.

Lipsyte: Yeah, Lish would say, "That's bullshit!"

If they did this for statistics articles, I think they'd rarely get past the abstract, most of the time. The methods are so poorly motivated. You're doing a so-called "exact test" because . . . why? And that "uniformly most powerful test" is a good idea because . . . why again? Because "power" is good? And that "Bayes factor"? Etc.

The rest here.

Andrew Gelman on religious affiliation of Supreme Court justices. With graphs.

what an attractor is all about

It appears that the eigenvalue distribution is an attractor. That is, for a broad range of different input models (distributions of the random matrices), you get the same output--the same eigenvalue distribution--as the sample size becomes large. This is interesting, and it's hard to prove. (At least, it seemed hard to prove the last time I looked at it, about 20 years ago, and I'm sure that it's even harder to make advances in the field today!)

Now, to return to the news article. If the eigenvalue distribution is an attractor, this means that a lot of physical and social phenomena which can be modeled by eigenvalues (including, apparently, quantum energy levels and some properties of statistical tests) might have a common structure. Just as, at a similar level, we see the normal distribution and related functions in all sorts of unusual places.

Consider this quote from Buchanan's article:

Recently, for example, physicist Ferdinand Kuemmeth and colleagues at Harvard University used it to predict the energy levels of electrons in the gold nanoparticles they had constructed. Traditional theories suggest that such energy levels should be influenced by a bewildering range of factors, including the precise shape and size of the nanoparticle and the relative position of the atoms, which is considered to be more or less random. Nevertheless, Kuemmeth's team found that random matrix theory described the measured levels very accurately.

That's what an attractor is all about: different inputs, same output.

Andrew Gelman on random matrices, the rest here

ahem

Andrew Gelman casts a critical eye on John Lanchester's analysis of UK electoral districts and supposed advantage to Labour of these as currently drawn up. (Er, we at paperpools blithely linked to the post on the theory, to which we are still partial, that we can never get too much of John Lanchester.)

1989

I firmly believe that a person such as myself who can't make music (aside from drumming, tuneless singing, etc.) can't understand it in the way that a practitioner can. In general, I think it's hard to understand a system that you can't perturb.

Andrew Gelman, in a comment on a comment on a post on Joshua Clover's 1989.

lots and lots of fried ants revisited

Andrew Gelman links to this article by Gretchen Chapman and Jingjing Liu

Previous research has demonstrated that Bayesian reasoning performance is improved if uncertainty information is presented as natural frequencies rather than single-event probabilities. A questionnaire study of 342 college students replicated this effect but also found that the performance-boosting benefits of the natural frequency presentation occurred primarily for participants who scored high in numeracy. This finding suggests that even comprehension and manipulation of natural frequencies requires a certain threshold of numeracy abilities, and that the beneficial effects of natural frequency presentation may not be as general as previously believed.

Fans of Gigerenzer's Reckoning with Risk take note. (This is exactly what I need for book 7.71, so unbelievably great. Thanks, Andrew! And thanks, Keith, who told Andrew!)

a supposedly unfun thing

It goes like this: there's something you want to estimate and you have some data. Maybe, to take my favorite recent example, you want to break down support for school vouchers by religion, ethnicity, income, and state (or maybe you'd like to break it down even further, but you have to start somewhere).

Or maybe you want to estimate the difference between how rich and poor people vote, by state, over several decades--but you're lazy and all you want to work with are the National Election Studies, which only have a couple thousand respondents, at most, in any year, and don't even cover all the states.

Or maybe you want to estimate the concentration of cat allergen in a bunch of dust samples, while simultaneously estimating the calibration curve needed to get numerical estimates, all in the presence of contamination that screws up your calibration.

Addictive post by Andrew Gelman on That Modeling Feeling, the rest
here

Best Disclaimer in the World

Just to be clear, though, I didn't make these maps; I'm just linking to them.


Andrew Gelman clarifies a post on some excellent graphics with regrettable colorways.