Post a Comment On: cbloom rants

I believe that the modern trend of doing some very superficial data analysis to prove a point, or support your argument is extremely harmful. It leads to a false impression of a scientific basis to arguments that is in fact spurious. I've been thinking about this for a while, but this washingtonpost blog about the correlation of video games and gun violence recently popped into my blog feed, so I'll use it as an example. The Washington Post blog leads you to believe that the data shows an unequivocal lack of correlation between videogames and gun violence. That's nonsense. It only takes one glance at the chart to see that the data is completely dominated by other factors, like probably most strongly the gun ownership rate. You can't possibly try to find the effect of a minor contributing factor without normalizing for other factors, which most of these "analyses" fail to do, which makes them totally bogus. Furthermore, as usual, you would need a much larger sample size to have any confidence in the data, and you'd have to question the selection of data that was done. Also the entire thing being charted is wrong; it shouldn't be video game spending per capita, it should be video games played per capita (especially with China on there), and it shouldn't be gun-related murders, it should be all murders (because the fraction of murders that is gun related varies strongly by gun control laws, while the all murders rate varies more directly with the level of economic and social development in a country). (Using data and charts and graphs has been a very popular way to respond to the recent shootings. Every single one that I've seen is complete nonsense. People just want to make a point that they've previously decided, so they trot out some data to "prove it" or make it "non-partisan" as if their bogus charts somehow make it "factual". It's pathetic. Here's a good example of using tons of data to show absolutely nothing . If you want to make an editorial point, just write your opinion, don't trot out bogus charts to "back it up". ) It's extremely popular these days to "prove" that some intuition is wrong by finding some data that shows a reverse correlation. (blame Freakonomics, among other things). You get lots of this in the smarmy TED talks - "you may expect that stabbing yourself in the eye with a pencil is harmful, but in fact these studies show that stabbing yourself in the eye is correlated to longer life expectancy!" (and then everyone claps). The problem with all this cute semi-intellectualism is that it's very often just wrong. Aside from just poor data analysis, one of the major flaws with this kind of reasoning is the assumption that you are measuring all the inputs and all the outputs. An obvious case is education, where you get all kinds of bogus studies that show such-and-such program "improves learning". Well, how did you actually measure learning? Obviously something like cutting music programs out of schools "improves learning" if you measure "learning" in a myopic way that doesn't include the benefits of music. And of course you must also ask what else was changed between the measured kids and the control (selection bias, novelty effect, etc; essentially all the studies on charter schools are total nonsense since any selection of students and new environment will produce a short term improvement). I believe that choosing the wrong inputs and outputs is even worse than the poor data analysis, because it can be so hidden. Quite often there are some huge (bogus) logical leaps where the article will measure some narrow output and then proceed to talk about it as if it was just "better". Even when your data analysis was correct, you did not show it was better, you showed that one specific narrow output that you chose to measure improved, and you have to be very careful to not start using more general words. (one of the great classic "wrong output" mistakes is measuring GDP to decide if a government financial policy was successful; this is one of those cases where economists have in fact done very sophisticated data analysis, but with a misleadingly narrow output) Being repetitive : it's okay if you are actually very specific and careful not to extrapolate. eg. if you say "lowering interest rates increased GDP" and you are careful not to ever imply that "increased GDP" necessarily means "was good for the economy" (or that "was good for the economy" meant "was good for the population"); the problem is that people are sloppy, in their analysis and their implications and their reading, so it becomes "lowering interest rates improved the well-being of the population" and that becomes accepted wisdom. Of course you can transparently see the vapidity of most of these analyses because they don't propagate error bars. If they actually took the errors of the measurement, corrected for the error of the sample size, propagated it through the correlation calculation and gave a confidence at the end, you would see things like "we measured a 5% improvement (+- 50%)" , which is no data at all. I saw Bryan Cox on QI recently, and there was some point about the US government testing whether heavy doses of LSD helped schizophrenics or not. Everyone was aghast but Bryan popped up with "actually I support data-based medicine; if it had been shown to help then I would be for that therapy". Now obviously this was a jokey context so I'll cut Cox some slack, but it does in fact reflect a very commonly held belief these days (that we should trust the data more than our common sense that it's a terrible idea). And it's just obviously wrong on the face of it. If the study had shown it to help, then obviously something was wrong with the study. Medical studies are almost always so flawed that it's hard to believe any of them. Selection bias is huge, novelty and placebo effect are huge; but even if you really have controlled for all that, the other big failure is that they are too short term, and the "output" is much too narrow. You may have improved the thing you were measuring for, but done lots of other harm that you didn't see. Perhaps they did measure a decrease in certain schizophrenia symptoms (but psychotic lapses and suicides were way up; oops that wasn't part of the output we measured). Exercise/dieting and child-rearing are two major topics where you are just bombarded with nonsense pseudo-science "correlations" all the time. Of course political/economic charts are useless and misleading. A classic falsehood that gets trotted out regularly is the charts showing "the economy does better under democrats" ; for one thing the sample size is just so small that it could be totally random ; for another the economy is more effected by the previous president than the current ; and in almost every case huge external factors are massively more important (what's the Fed rate, did Al Gore recently invent the internet, are we in a war or an oil crisis, etc.). People love to show that chart but it is *pure garbage* , it contains zero information. Similarly the charts about how the economy does right after a tax raise or decrease; again there are so many confounding factors and the sample size is so tiny, but more importantly tax raises tend to happen when government receipts are low (eg. economic growth is already slowing), while tax cuts tend to happen in flush times, so saying "tax cuts lead to growth" is really saying "growth leads to growth". What I'm trying to get at in this post is not the ridiculous lack of science in all these studies and "facts", but the way that the popular press (and the semi-intellectual world of blogs and talks and magazines) use charts and graphs to present "data" to legitimize the bogus point. I believe that any time you see a chart or graph in the popular press you should look away. I know they are seductive and fun, and they give you a vapid conversation piece ("did you know that christmas lights are correlated with impotence?") but they in fact poison the brain with falsehoods. Finally, back to the issue of video games and violence. I believe it is obvious on the face of it that video games contribute to violence. Of course they do. Especially at a young age, if a kid grows up shooting virtual men in the face it has to have some effect (especially on people who are already mentally unstable). Is it a big factor? Probably not; by far the biggest factor in violence is poverty, then government instability and human rights, then the gun ownership rate, the ease of gun purchasing, etc. I suspect that the general gun glorification in America is a much bigger effect, as is growing up in a home where your parents had guns, going to the shooting range as a child, rappers glorifying violence, movies and TV. Somewhere after all that, I'm sure video games contribute. The only thing we can actually say scientifically is that the effect is very small and almost impossible to measure due to the presence of much larger and highly uncertain factors. (of course we should also recognize that these kind of crazy school shooting events are completely different than ordinary violence, and statistically are a drop in the bucket. I suspect the rare mass-murder psycho killer things are more related to a country's mental health system than anything else. Pulling out the total murder numbers as a response to these rare psychotic events is another example of using the wrong data and then glossing over the illogical jump.) I think in almost all cases if you don't play pretend with data and just go and sit quietly and think about the problem and tap into your own brain, you will come to better conclusions.