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by jspiral
1034 days ago
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what works for me is to think of it like the mob of people is already there in front of you, and you're first grabbing out just a subset of the mob based on some conditions, then checking only those qualified at random which is the same as checking the distributions inside the qualified group.
so for problem 1, the mob is all parents with 2 children, each child is either b or g. the underlying distribution is 25%bb, 25%gg, 50%bg. you first cull the set by saying "only parents with at least 1 b, line up to be examined". i think it's clear that the selected population will be 1/3 bb parents, 2/3 bg parents, right? on the other hand the second problem is you saying: "all parents with a b born on tuesday, report to be examined!". note that most of the parents that were in the question 1 selected population are now excluded. try to imagine which parents get selected by this one. you can't make the inference back to the original question because it's a different question about a broader group of people. question 1 population distrubution actually is 1/3 vs 2/3. the key is to think of it as selecting different subsets of the parent mob. |
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But we expect about the same number of parents to show up for "all parents with a b born on Tuesday" as "all parents with a b born on Monday." This feels like some kind of Simpson's paradox I've invented where every subgroup has a the same outcome, which is different than the outcome of the group as whole.