Generally, the answer to your question is Bayes’ Theorem. This theorem is essentially the mathematical formulation of how evidence ought to be weighed when testing ideas. If the wikipedia article doesn’t help you much, Eliezer has written an in-depth explanation of what it is and why it works.
The specific answer to your question can be revealed by plugging into this equation, and defining “proof”. We say that nothing is ever “proven” to 100% certainty, because if it were (again, according to Bayes’ Theorem), no amount of new evidence against it could ever refute it. So “proof” should be interpreted as “really, really likely”. You can pick a number like “99.9% certain” if you like. But your best bet is to scrap the notion of absolute “proof” and start thinking in likelihoods.
You’ll notice that an integral part of Bayes’ Theorem is the idea of how strongly we would expect to see a certain piece of evidence. If the Hypothesis A is true, how likely is it that we’ll see Evidence B? And additionally, how likely would it be to see Evidence B regardless of Hypothesis A?
For a piece of evidence to be strong, it has to be something that we would expect to see with much greater probability if a hypothesis is true than if it is false. Otherwise there’s a good chance it’s a fluke. Furthermore, if that evidence is something that we wouldn’t expect to see much either way, than it’s not very informative when we don’t see it.
So you see how this bears on your examples. I’m not especially familiar with astronomy, so I don’t know whether it’s true that we haven’t seen other galaxies with planets, or how powerful our telescopes are. But let’s assume that what you’ve said is all true.
If we know our telescopes aren’t powerful enough to see other planets, then the fact that they don’t see any is virtually zero evidence. The probability of us seeing other planets is basically the same whether they’re out there or not (because we won’t see them either way), so our inability to see them doesn’t count as evidence at all. This test doesn’t actually tell us anything because we already know that it will tell us the same thing either way. It’s like counting how many fingers you have to determine if the stock market will go up or down. You’re gonna get “ten” no matter what, and this tells you nothing about the market.
The same reasoning applies to the bacteria example. If we’re not more likely to see them given that they’re real than we are given that they’re not real, then our inability to see them is not evidence in either direction. The test is a bad one because it fails to distinguish one possibility from the other.
But all this isn’t to say that it would be valid to reject these notions based on the absence of these evidences alone. There may be other tests we can run that would be more likely to come out one way or the other based on whether the hypothesis is true. So no, it wouldn’t make sense to reject the existence of planets or bacteria, because in both of your examples people are using tests that are known to be useless.
If we’re not more likely to see them given that they’re real than we are given that they’re not real, then our inability to see them is not evidence in either direction. The test is a bad one because it fails to distinguish one possibility from the other
For a sense of scale: the most distant extrasolar planet is 21,500 ± 3,300 light years away, and rather hypothetical—look at the size of the error bar on that distance.
Hi DevilMaster, welcome to LessWrong!
Generally, the answer to your question is Bayes’ Theorem. This theorem is essentially the mathematical formulation of how evidence ought to be weighed when testing ideas. If the wikipedia article doesn’t help you much, Eliezer has written an in-depth explanation of what it is and why it works.
The specific answer to your question can be revealed by plugging into this equation, and defining “proof”. We say that nothing is ever “proven” to 100% certainty, because if it were (again, according to Bayes’ Theorem), no amount of new evidence against it could ever refute it. So “proof” should be interpreted as “really, really likely”. You can pick a number like “99.9% certain” if you like. But your best bet is to scrap the notion of absolute “proof” and start thinking in likelihoods.
You’ll notice that an integral part of Bayes’ Theorem is the idea of how strongly we would expect to see a certain piece of evidence. If the Hypothesis A is true, how likely is it that we’ll see Evidence B? And additionally, how likely would it be to see Evidence B regardless of Hypothesis A?
For a piece of evidence to be strong, it has to be something that we would expect to see with much greater probability if a hypothesis is true than if it is false. Otherwise there’s a good chance it’s a fluke. Furthermore, if that evidence is something that we wouldn’t expect to see much either way, than it’s not very informative when we don’t see it.
So you see how this bears on your examples. I’m not especially familiar with astronomy, so I don’t know whether it’s true that we haven’t seen other galaxies with planets, or how powerful our telescopes are. But let’s assume that what you’ve said is all true.
If we know our telescopes aren’t powerful enough to see other planets, then the fact that they don’t see any is virtually zero evidence. The probability of us seeing other planets is basically the same whether they’re out there or not (because we won’t see them either way), so our inability to see them doesn’t count as evidence at all. This test doesn’t actually tell us anything because we already know that it will tell us the same thing either way. It’s like counting how many fingers you have to determine if the stock market will go up or down. You’re gonna get “ten” no matter what, and this tells you nothing about the market.
The same reasoning applies to the bacteria example. If we’re not more likely to see them given that they’re real than we are given that they’re not real, then our inability to see them is not evidence in either direction. The test is a bad one because it fails to distinguish one possibility from the other.
But all this isn’t to say that it would be valid to reject these notions based on the absence of these evidences alone. There may be other tests we can run that would be more likely to come out one way or the other based on whether the hypothesis is true. So no, it wouldn’t make sense to reject the existence of planets or bacteria, because in both of your examples people are using tests that are known to be useless.
If we’re not more likely to see them given that they’re real than we are given that they’re not real, then our inability to see them is not evidence in either direction. The test is a bad one because it fails to distinguish one possibility from the other
Thank you. That’s what I did not understand.
For a sense of scale: the most distant extrasolar planet is 21,500 ± 3,300 light years away, and rather hypothetical—look at the size of the error bar on that distance.
The nearest dwarf satellite galaxy is 25,000 light years away, so I suppose we’ve got a chance of seeing planets there.
The nearest actual galaxy is Andromeda, at 2.5 million light years.