IlyaShpitser comments on How urgent is it to intuitively understand Bayesianism? - Less Wrong
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I work in tech support (pretty advanced, i.e. I'm routinely dragged into conference calls on 5 minutes notice with 10 people in panic mode because some database cluster is down). Here's a standard situation: "All queries are slow. There are some errors in the log saying something about packets dropped.". So, do I go and investigate all network cards on these 50 machines to see if the firmware is up to date, or do I look for something else? I see people picking the first option all the time. There are error messages, so we have evidence, and that must be it, right? But I have prior knowledge: it's almost never the damn network, so I just ignore that outright, and only come back to it if more plausible causes can be excluded.
Bayes gives me a formal assurance that I'm right to reason this way. I don't really need it quantitatively - just repeating "Base rate fallacy, base rate fallacy" to myself gets me in the right direction - but it's nice to know that there's an exact justification for what I'm doing. Another way would be to learn tons of little heuristics ("No. It's not a compiler bug.", "No. There's not a mistake in this statewide math exam you're taking"), but it's great to look at the underlying principle.
Troubleshooting is a great example where a little probability goes a long way, thanks.
Amusingly, there was in fact an error in the GRE Subject test I once took, long ago (in computer science). All of the 5 multiple choice answers were incorrect. I agree that conditional on disagreement between test and testtaker, the test is usually right.
The Rasch model does not hate truth, nor does it love truth, but the truth if made out of items which it can use for something else.