I often run into this phrase.. Correlation does not imply causation.
It is a shorthand for: “Just because A and B are correlated, it does not prove that A causes B.” (Because that is the conclusion many people automatically do.)
Something about it bugs me, intuitively.
See this xkcd comic, especially the mouseover text for the image.
Which for this audience is like blasphemy. Intuition that is.
I believe there should be a mathematical method for calculating an estimate of the probability that a correlation is coincidential.
There is a chance it could be somewhere in this book. I am not sure about it, because I didn’t read the book, just heard about it.
By the way, I am not sure if your example also include observer-caused correlation (I don’t know how it is officially called), where the correlation between the observed events is caused by the method how the observer selects them. For example, imagine that there are nine objects, let’s call them A1, A2, A3, B1, B2, B3, C1, C2, C3. For some reason, you are only interested about properties A and 1. So you only notice the objects A1, A2, A3, B1, C1, and ignore all the rest. Now you observe that in you selected set, the properties A and 1 are anticorrelated. But this anticorrelation is not a property of the original set, only of your observed subset; so it explains something about you, not about the original data. (In real life known as: “Why are all the attractive [insert the sex you find attractive] so [insert a noticeable negative trait]?”.)
A suggestion for future posts: If you notice something really really obvious, please assign higher probability that someone else noticed it too. It doesn’t mean you shouldn’t say it, but you can probably skip the long introduction before you get to the topic. ;-)
I think I’m on the verge of deleting this post because :D I mean I did feel like well gee, this gotta be pretty obvious for someone who actually works with this stuff but… I just couldn’t help it :D
The introduction surely was very long but.. I guess it worked to soften the landing.. err, FALL, a bit … :D
It is a shorthand for: “Just because A and B are correlated, it does not prove that A causes B.” (Because that is the conclusion many people automatically do.)
See this xkcd comic, especially the mouseover text for the image.
See “Your Strength as a Rationalist ”, specifically the last paragraph.
There is a chance it could be somewhere in this book. I am not sure about it, because I didn’t read the book, just heard about it.
By the way, I am not sure if your example also include observer-caused correlation (I don’t know how it is officially called), where the correlation between the observed events is caused by the method how the observer selects them. For example, imagine that there are nine objects, let’s call them A1, A2, A3, B1, B2, B3, C1, C2, C3. For some reason, you are only interested about properties A and 1. So you only notice the objects A1, A2, A3, B1, C1, and ignore all the rest. Now you observe that in you selected set, the properties A and 1 are anticorrelated. But this anticorrelation is not a property of the original set, only of your observed subset; so it explains something about you, not about the original data. (In real life known as: “Why are all the attractive [insert the sex you find attractive] so [insert a noticeable negative trait]?”.)
A suggestion for future posts: If you notice something really really obvious, please assign higher probability that someone else noticed it too. It doesn’t mean you shouldn’t say it, but you can probably skip the long introduction before you get to the topic. ;-)
Thanks for your comment :)
I think I’m on the verge of deleting this post because :D I mean I did feel like well gee, this gotta be pretty obvious for someone who actually works with this stuff but… I just couldn’t help it :D
The introduction surely was very long but.. I guess it worked to soften the landing.. err, FALL, a bit … :D