Agreed that evaluating the relevance of an argument A to the truth-value T of a proposition doesn’t depend on knowing T.
Agreed that pointing out to people who are invested in a particular value of T and presenting A to justify T that in fact A isn’t relevant to T generally pisses them off.
Agreed that if T is binary, there are more possible As unrelated to T than there are wrong possible values for T, which means my chances of randomly getting a right answer about T are higher than my chances of randomly constructing an argument that’s relevant to T. (But I’ll note that not all interesting T’s are binary.)
the drug example, there is no unambiguous conclusion, and one can be ambivalent,
This statement confuses me.
If I look at all my data in this example, I observe that the drug did better than placebo half the time, and worse than placebo half the time. This certainly seems to unambiguously indicate that the drug is no more effective than the placebo, on average.
Is that false for some reason I’m not getting? If so, then I’m confused
If that’s true, though, then it seems my original formulation applies. That is, evaluating all the evidence in this case leads me unambiguously to conclude “the drug is no more effective than the placebo, on average”. I could pick subsets of that data to argue both “the drug is more effective than the placebo” and “the drug is less effective than the placebo” but doing so would be unambiguously lying.
Which seems like a fine example of “in cases where evaluating all the evidence I have leads me unambiguously to conclude A, and then I pick only that subset of the evidence that leads me to conclude NOT A, I’m unambiguously lying.” No? (In this case, the A to which my evidence unambiguously leads me is “the drug is no more effective than the placebo, on average”
If I look at all my data in this example, I observe that the drug did better than placebo half the time, and worse than placebo half the time. This certainly seems to unambiguously indicate that the drug is no more effective than the placebo, on average.
but would it seem if it was 10 trials, 5 win 5 lose? It just sets some evidence that effect is small. If the drug is not some homoeopathy thats pure water, you shouldn’t privilege zero effect. Exercise for the reader: calculate 95% ci for 100 placebo-controlled trials.
Ah, I misunderstood your point. Sure, agreed that if there’s a data set that doesn’t justify any particular conclusion, quoting a subset of it that appears to justify a conclusion is also lying.
Well, the same should apply to arguing a point when you could as well have argued opposite with same ease.
Note, as you said:
In most real-world cases, both true statements and false statements have evidence in favor of them
and i made an example where both true and false statements got “evidence in favour of them” − 50 trials one way, 50 trials other way. Both of those evidences are subset of evidence, that appears to justify a conclusion, and is a lie.
Agreed that evaluating the relevance of an argument A to the truth-value T of a proposition doesn’t depend on knowing T.
Agreed that pointing out to people who are invested in a particular value of T and presenting A to justify T that in fact A isn’t relevant to T generally pisses them off.
Agreed that if T is binary, there are more possible As unrelated to T than there are wrong possible values for T, which means my chances of randomly getting a right answer about T are higher than my chances of randomly constructing an argument that’s relevant to T. (But I’ll note that not all interesting T’s are binary.)
This statement confuses me.
If I look at all my data in this example, I observe that the drug did better than placebo half the time, and worse than placebo half the time. This certainly seems to unambiguously indicate that the drug is no more effective than the placebo, on average.
Is that false for some reason I’m not getting? If so, then I’m confused
If that’s true, though, then it seems my original formulation applies. That is, evaluating all the evidence in this case leads me unambiguously to conclude “the drug is no more effective than the placebo, on average”. I could pick subsets of that data to argue both “the drug is more effective than the placebo” and “the drug is less effective than the placebo” but doing so would be unambiguously lying.
Which seems like a fine example of “in cases where evaluating all the evidence I have leads me unambiguously to conclude A, and then I pick only that subset of the evidence that leads me to conclude NOT A, I’m unambiguously lying.” No? (In this case, the A to which my evidence unambiguously leads me is “the drug is no more effective than the placebo, on average”
Non-binary T: quite so, but can be generalized.
but would it seem if it was 10 trials, 5 win 5 lose? It just sets some evidence that effect is small. If the drug is not some homoeopathy thats pure water, you shouldn’t privilege zero effect. Exercise for the reader: calculate 95% ci for 100 placebo-controlled trials.
Ah, I misunderstood your point. Sure, agreed that if there’s a data set that doesn’t justify any particular conclusion, quoting a subset of it that appears to justify a conclusion is also lying.
Well, the same should apply to arguing a point when you could as well have argued opposite with same ease.
Note, as you said:
and i made an example where both true and false statements got “evidence in favour of them” − 50 trials one way, 50 trials other way. Both of those evidences are subset of evidence, that appears to justify a conclusion, and is a lie.
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You are absolutely correct.
Point taken.