# Alan H.

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bio website location age member for 3 years, 4 months seen Oct 8 '12 at 18:43 profile views 51

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 Nov15 awarded Nice Question Aug27 awarded Taxonomist Jul19 awarded Yearling Jul18 awarded Favorite Question May8 awarded Notable Question Sep30 comment Using confidence interval from full sample to test statistics from a small sub-sample? In other words, what I am expecting to find is that the subgroup is not predictable and does not have the same distribution compared to the group at large, precisely because the subgroup is not random in a certain theoretically interesting way. Sep30 comment Using confidence interval from full sample to test statistics from a small sub-sample? @MichaelChernick: Thank you for your comments. Let's say we don't know that Chicago is more democratic, and that's what we want to know. You find that 2/5 people in the state are democrats. You get a confidence interval, and then find that in Chicago the proportion is higher than that confidence interval, would it be valid to say that Chicago is in fact more democratic than the state at large? Keeping in mind that this is all from the same poll. I'm not trying to predict anything as such, but merely trying to tell if the subgroup is in fact significantly different from the group overall. Sep28 asked Using confidence interval from full sample to test statistics from a small sub-sample? Sep27 awarded Popular Question Jul30 accepted Standard measure of clumpiness? Jul29 asked Standard measure of clumpiness? Jul24 awarded Popular Question Jul19 awarded Yearling May1 awarded Good Question Apr10 accepted Explaining to laypeople why bootstrapping works Apr9 comment Explaining to laypeople why bootstrapping works @cardinal Thanks, I updated the original post. Hopefully it is more clear. :) Apr9 awarded Nice Question Apr9 comment Explaining to laypeople why bootstrapping works I see, so if I understand you, then this technique assumes that the sample is an adequate model of the population, and therefore that resampling over that sample on a large enough scale will reveal something about the population, but only to the extent that the original sample is a good one. Now that I put it that way it seems almost obvious... Apr8 accepted Pitfalls of linear mixed models Apr8 comment Explaining to laypeople why bootstrapping works Thanks! This much I understand. I am particularly wondering how it is that resampling from a sample of the population helps to understand the underlying population. If we are resampling from a sample, how is it that we are learning something about the population rather than only about the sample? There seems to be a leap there which is somewhat counter-intuitive.