[Link] Correlation Graphs Reveal Shocking Information

I do worry sometimes that the pendulum has swung too far in the other direction, and that people are starting using correlation-causation as an I'm-smarter-than-you sort of status signal - that is, once people pass a certain intelligence level I worry less about them claiming Facebook causes the Greek debt crisis because they're correlated, and more about them hearing a very well-conducted study showing an r = .98 correlation between some disease and some risk factor, and instead of agreeing we should investigate further they just say "HA! GOTCHA! CORRELATION'S NOT THE SAME THING AS CAUSATION!"

I mean, I admit it's an important lesson, as long as people remember it's just a caution against being too certain of a causal relationship, and not a guarantee that a correlation provides absolutely no evidence.

I don’t think these are very good examples. Those lines hardly look correlated, let alone casually related. I once read an article with a much better example, but I can’t find it now. It first talked about how if you looked through enough examples you could find any correlation, and then showed a very closely correlated graph of the stock market versus something about Venus, like its surface temperature or distance from the sun or something.

You can easily generate correlation examples with Google Correlate, such as how AppleWorks is causing the decline of the Japanese language.

I don't in any way intend this as a criticism, but Mark Twain's complaint about bringing up the weather in conversation seems to apply here: everyone talks about it, but nobody ever does anything about it.

The problem with just saying "correlation is not causation" is that it doesn't help you figure out what information you can get from an observational study. This is becoming an important issue in my work. We do advanced marketing research for (mostly) large companies. For many years the company's emphasis was on choice-based conjoint studies, which give you experimental data. (You ask people to repeatedly choose among various sets of hypothetical products, and then analyze the results to figure out what they value.) Now we're moving more into marketing mix models, which involve purely observational data. Knowing exactly what one can and cannot legitimately infer from observational data, under what assumptions, is a very important practical question for us and our clients.

That's why I am currently spending a lot of my spare time studying Judea Pearl's book Causality. I would highly recommend this book to anyone who, as Yvain suggests, is more interested in solving problems than looking smart.

It's these fallacies that make reading newspapers almost impossible without becoming incredibly frustrated incredible quickly. I imagine the first media editor that realised they could miscontrue data to support their articles almost collapsed with excitement.

"Oh my god guys! Look, now we can weaponize our hyperbole!!"

LW needs a (Funny) tag like Slashdot. I'm saving this for future use in dispelling the correlation/causation fallacy.