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Sometimes I think this would be a lot easier for everyone if we just hauled out Bayes' theorem and had done with it. If a hypothesis (weighted with your confide
by pjscott 16y ago
Sometimes I think this would be a lot easier for everyone if we just hauled out Bayes' theorem and had done with it. If a hypothesis (weighted with your confidence in it) predicts that a set of observations would turn up more evidence for it than you actually find, then that is evidence against that hypothesis, and should cause you to lower your estimate of the probability that the hypothesis is correct. And vice versa.
For example, suppose that I want to explain why my apples have been stolen from my apartment. I live in a hypothetical universe where there are only three possibilities, all of which have roughly the same likelihood, a priori: spontaneous combustion, alien abduction, or theft by a troupe of dastardly harlequins.
If the apples combusted, I would expect to see scorch marks, or perhaps soot stains on the wall nearby. If they were abducted by aliens, I would not expect to see anything, since everyone knows that alien abduction rays leave no signs. Finally, if the vile harlequins made off with my fruit, I would expect to see some signs of their passing -- face-paint on the doorknob, perhaps a fool's cap hastily discarded in a corner.
Now, I inspect my apartment. I find no scorch marks, which is evidence against spontaneous combustion. If my apples had burst into flame, it's technically possible that they could have done so without leaving marks, but unlikely. I find no evidence of alien abduction, which is exactly what I would expect to find, so it does not change my estimate of that hypothesis' likelihood. Finally, I find a scrap of ripped, brightly-colored cloth which appears to have been torn from the outfit of an interloper hastily scurrying away through my pointlessly-large ventilation ducts. My face grows grim as I raise my estimate of the likelihood of my third hypothesis. Harlequins! Foul creatures of the Commedia dell'arte! My apartment has been defiled by their acrobatics and shenanigans! And they stole my darn apples!
So, in the case of spontaneous combustion, absence of evidence was evidence of absence. For alien fruit abduction, absence of evidence wasn't evidence of anything. In the third case, there was evidence above what I would expect from my prior beliefs, so that was evidence for the harlequin hypothesis.
- _delirium 16y agoI buy that, but I guess to me that is what the slogan is supposed to imply, but I do agree it's not always taken that way. That is: merely not finding evidence tells you nothing. However, not finding evidence, when you have a prediction of what evidence should be found, may be evidence against. So you need two things there: 1) absence of evidence; and 2) a prediction of what evidence there should've been. If all you have is #1 by itself, you have no evidence of anything. Hence, absence of evidence (without more) isn't evidence of absence. Maybe I'm in the wrong debates, but most of the times when I see the slogan applied, the person doesn't have the second part. For example, a common elementary error is to conclude that variables are uncorrelated because you calculated their correlation in your data set and it was not significant. Failure to find a correlation in a very large population study that's large enough that it should have found one, can be evidence of no correlation (or no large correlation, at least), but you have to do a separate analysis for that.