Cardiologists and Chinese Robbers

There are over a billion Chinese people. If even one in a thousand is a robber, you can provide one million examples of Chinese robbers to appease the doubters. Most people think of stereotyping as “Here’s one example I heard of where the out-group does something bad,” and then you correct it with “But we can’t generalize about an entire group just from one example!” It’s less obvious that you may be able to provide literally one million examples of your false stereotype and still have it be a false stereotype. If you spend twelve hours a day on the task and can describe one crime every ten seconds, you can spend four months doing nothing but providing examples of burglarous Chinese – and still have absolutely no point.

If we’re really concerned about media bias, we need to think about Chinese Robber Fallacy as one of the media’s strongest weapons. There are lots of people – 300 million in America alone. No matter what point the media wants to make, there will be hundreds of salient examples. No matter how low-probability their outcome of interest is, they will never have to stop covering it if they don’t want to.

Cardiologists and Chinese Robbers

Marginal Revolution: Sex and Statistics or Heteroscedasticity is Hot

Alex Tabarrok mulls over the recent OkTrends post on the Mathematics of Beauty.

I think there are certain types of beauty that greatly attract some men but repel others. Analagously, some people will pay hundreds of dollars for an ounce of caviar that other people won’t eat for free. The reason some people love caviar, however, is not that other people dislike it. Instead, it just so happens, that the thing that some people love is the very thing that repels others. We see the same phenomena in art, some people love John Cage, other people would rather listen to nothing at all. ;)

Now if we mix in this kind of beauty–beauty over which there are violent disagreements–with the kind that most people do agree upon (think Haagan-Dazs vanilla ice cream) then I suspect that it will appear that lower rankings increase messages. But what is really going on is that high rankings–conditional on their also being many low rankings–actually signal an extra strong attraction. Someone who tells you that John Cage is their favorite composer is telling you more than someone who says Aaron Copland is their favorite composer.

Marginal Revolution: Sex and Statistics or Heteroscedasticity is Hot

Marginal Revolution: Sex and Statistics or Heteroscedasticity is Hot

Alex Tabarrok mulls over the recent OkTrends post on the Mathematics of Beauty.

I think there are certain types of beauty that greatly attract some men but repel others. Analagously, some people will pay hundreds of dollars for an ounce of caviar that other people won’t eat for free. The reason some people love caviar, however, is not that other people dislike it. Instead, it just so happens, that the thing that some people love is the very thing that repels others. We see the same phenomena in art, some people love John Cage, other people would rather listen to nothing at all. ;)

Now if we mix in this kind of beauty–beauty over which there are violent disagreements–with the kind that most people do agree upon (think Haagan-Dazs vanilla ice cream) then I suspect that it will appear that lower rankings increase messages. But what is really going on is that high rankings–conditional on their also being many low rankings–actually signal an extra strong attraction. Someone who tells you that John Cage is their favorite composer is telling you more than someone who says Aaron Copland is their favorite composer.

Marginal Revolution: Sex and Statistics or Heteroscedasticity is Hot

Graph of the year – Statistical Modeling, Causal Inference, and Social Science.

Bill James (and others) have pointed out that true racial equality in baseball came, not when superstars such as Jackie Robinson and Willie Mays started joining major league rosters, but when there was room for ordinary black players to join their equally unexceptional white colleagues on the bench.

Similarly, graphical methods have truly arrived when journalists use graphs to make ordinary, unexceptional points in a clearer way. When making a graph, and including it in an article, is easy enough that it’s done as a matter of course.

I love this post about measuring whether an artist is under- or over-valued. The method is pretty cool, basically comparing the Human Accomplishment ranking and the available Amazon music inventory, and making a rough P/E ratio. This post focuses on notable composers and it looks like Medieval, Renaissance, and Baroque composers get shorted, while late Romantics (especially opera dudes) get more hype than they deserve. And you see the same sort of bias in the season programming of most major orchestras.
Anyway, two cool things this brings to mind. One, I like this idea of bubbles in culture. Reminds me of the vast difference in New York Times coverage of conflicts in Darfur vs. the Congo, though one area has been about 10 times as deadly. There are all kinds of interesting feedback loops that affect how we perceive and respond to our world. And two, realizing that there’s so much rough-and-ready data out there that we’ve unwittingly created, just waiting to be mined.