The History of 'Correlation Does Not Imply Causation' 223
Dr Herbert West writes "The phrase 'correlation does not imply causation' goes back to 1880 (according to Google Books). However, use of the phrase took off in the 1990s and 2000s, and is becoming a quick way to short-circuit certain kinds of arguments. In the late 19th century, British statistician Karl Pearson introduced a powerful idea in math: that a relationship between two variables could be characterized according to its strength and expressed in numbers. An exciting concept, but it raised a new issue: how to interpret the data in a way that is helpful, rather than misleading. When we mistake correlation for causation, we find a cause that isn't there, which is a problem. However, as science grows more powerful and government more technocratic, the stakes of correlation — of counterfeit relationships and bogus findings — grow larger."
Re:Maybe (Score:3, Interesting)
Re:Correlation != causation. (Score:5, Interesting)
Correlation may not lead to causation... However it tends to give a clue on the causation.
For example a Correlation between the number of tattoos vs. the number of Motorcycle accidents.
Well ink in your skin doesn't cause you to get in an accident. However people who are more apt to taking risks will more likely get a tattoo. People who take more risks get into accidents more.
In terms of policy, you want to reduce motorcycle accidents, telling people you need to stop getting tattoos will not be effective. However with this correlation you may get results by posting motorcycle safety information at the tattoo parlors.
But using Correlation != causation as a way to short circuit an argument isn't that effective. Because if your goal is to dig for the truth or a solution, the correlation is important, and if the correlation seems reasonable to create the causation it is worth further investigation.
absence of evidence is not evidence of absence (Score:4, Interesting)
This reply usually confuses them enough to go away.
Re:antiscience (Score:2, Interesting)
IMHO, the reason the expression is used so frequently is because we've recently become a more data-oriented society. Causation is the simplest explanation for correlation, so people are apt to overuse it and read too much into the data. Correlation with a plausible theory, preferably a testable one, is useful knowledge. Without such a theory, the correlation itself is trivial (unless you're an expert forming a theory). Thinking it's something profound and significant is like predicting the stock market from butter production in Bangladesh, which people have actually tried since they don't understand the limitations of correlation nor the meaning of satire.
Observational data alone is very weak. Randomized controlled trials are better, but they're usually limited in scope and somewhat weak until it can be replicated by third parties. For example, drug companies fund very large and powerful studies for new medications, yet it's fairly common for the conclusions to be overturned as science progresses. That's an inherent weakness of testing a complex and non-isolatable system like the human body. Nutrition is even worse, as you can't well tell people to eat an experimental diet for 30 years to measure the health effects (hence why nutritional recommendations change all the time, e.g. saturated fat or salt and high blood pressure). Frankly, climatology scares me, as it makes broad, untestable predictions on mostly observational data of a single system. I understand that it's frustrating if people aren't convinced with mere correlation, but IMHO that's just being rational.