```Date: Sep 29, 2010 3:39 PM
Author: cagdas.ozgenc@gmail.com
Subject: Re: Sampling From Finite Population with Replacement

On 29 Eylül, 23:09, Rich Ulrich <rich.ulr...@comcast.net> wrote:> On Tue, 28 Sep 2010 21:21:42 -0700 (PDT), Cagdas Ozgenc>> <cagdas.ozg...@gmail.com> wrote:>> >> I agree that the above is ambiguous if you really want to press> >> the point.  It uses mu and sigma which describe populations> >> It  does not state whether the population is the class of 2005,> >> or something wider that would be more useful for generalization.>> >That's not the issue. Take any finite population with a data> >generating process behind it. Population mean is an unbiased estimate> >of data generating process distribution as Ray pointed out. But once> >you start getting samples from that population your random error turns> >into a systematic error (a bias).>> That's clever, but basically wrong.  That is not the definition> of bias that we have in play previously.>> You can get closer and closer to obtaining the value of> the population mean;  but you never have more precision> than what the population mean provides, in regards to> estimating the underlying process.  >> So?  That is the error of a single sampling (the "population").>> Yes, colloquially speaking, we say that any single drawing of> a sample is going to be biased, or it gives a biased estimate.> But the relevant meaning when we speak of "an unbiased> statistic"  is limited to the venue of the procedure that is being> repeated.  >> Subsamples give an unbiased estimate of the sample.> The sample gives an unbiased estimate of the generating> process -- and the mean of the whole sample has smaller error> than any of its subsamples will have.  Technically, we want to> say that subsamples *do* give an unbiased estimate of the> generating process, (inevitably) with larger error than the> whole sample.  >> The prospect of mis-statement arises from imagining that> using the subsamples can escape the original error of the> sample.  Even though we may casually call it "biased" when> we describe its effect, that is applying the adjective on a> different level of intercourse.>> --> Rich UlrichI am glad that we are now at least on the same ground.If I look at the definition of Sampling Bias in Wikipedia it isactually exactly what you describe above.http://en.wikipedia.org/wiki/Sampling_biasEven though I understand you, I don't understand why you think I amwrong. Is it the definition of the word "bias" that we differ inopinion? If so according to which source (book, article, etc.)?
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