In many discussions I'm pulled back to the distinction between not-guilty and innocent as a way to demonstrate how the burden of proof works and what the true default position should be in any given argument. A lot of people seem to not have any problem seeing the distinction, but many intelligent people for some reason don't see it.
In this article I explain why the distinction exists and why it matters, in particular why it matters in real-life scenarios, especially when people try to shift the burden of proof.
Essentially, in my view the universe we are talking about is {uncertain,guilty,innocent}, therefore not-guilty is guilty', which is {uncertain,innocent}. Therefore innocent ⇒ not-guilty, but not-guilty ⇏ innocent.
When O. J. Simpson was acquitted, that doesn’t mean he was found innocent, it means the prosecution could not prove his guilt beyond reasonable doubt. He was found not-guilty, which is not the same as innocent. It very well could be that the jury found the truth of the matter uncertain.
This notion has implications in many real-life scenarios when people want to shift the burden of proof if you reject a claim when it's not substantiated. They wrongly assume you claim their claim is false (equivalent to innocent), when in truth all you are doing is staying in the default position (uncertain).
Rejecting the claim that a god exists is not the same as claim a god doesn't exist: it doesn't require a burden of proof because it's the default position. Agnosticism is the default position. The burden of proof is on the people making the claim.
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I know how expected value works. But this confirms what I said: a single percentage cannot tell me what I should believe.
Also, this still doesn't answer my scenario. Is the next toss of a coin going to land heads given that in previous instances there have been 50 heads / 50 tails? How about 0 heads / 0 tails?
I know there's a difference, but Bayesians assume they are the same.
The single value is just the point estimate of your belief. That belief also has a distribution over possible states with each state having it's own percentage attached to it.
The more times you flip a coin the more concentrated your probability distribution becomes around that coin being actually fair.
You seem to believe Bayesians only care about the point estimate and not the whole probability distribution. I don't think you disagree with Bayesianism so much as misunderstand what it is.
There is no "point estimate" of my belief because I don't believe anything.
You are trying pinpoint my belief on a continuum, or determine it with a probability function, but you can't, because I don't have any belief.
Do you have any source for that? Do you have any source that explains the difference between a coin flip with 0/0 priors vs 50/50?
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