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From |
David Greenberg <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Re: RE: when your sample is the entire population |

Date |
Fri, 18 Jan 2008 19:08:50 -0500 |

Michael, while it is true that program participants or school children could be considered a sample of a larger population, people are not usually in treatment programs or in particular schools at random, and the choice of institutions to study - this school rather than that school - are often not made at random either. So, one may have a sample but ordinarily not a simple random sample. Without taking selection effects into account, generalization to a larger population could be quite treacherous. David Greenberg, Sociology Department, New York University ----- Original Message ----- From: Michael Blasnik <[email protected]> Date: Friday, January 18, 2008 4:32 pm Subject: st: Re: RE: when your sample is the entire population To: [email protected] > I would just say -- "what Nick says" ;) > > But I'd like to emphasize one aspect related to his points 3 (and/or > 4) -- > measurement error. In many real applications, the outcome (and, > unfortunately, > the predictors) are measured with error. Therefore, you have > uncertainty even > with data for the full population. Also, the superpopulation concept > ( point 1) > seems quite reasonable -- at least for most program evaluation > questions where > you may collect data for all program participants (or kids in a > school) but they > can be considered a sample of some larger potential population. Of > course in > program evaluation you also still have uncertainty introduced by any > comparison/control group employed in the analysis. > > Michael Blasnik > > ----- Original Message ----- > From: "Nick Cox" <[email protected]> > To: <[email protected]> > Sent: Friday, January 18, 2008 3:02 PM > Subject: st: RE: when your sample is the entire population > > > >I guess most people will have a short answer and a long answer > > to this one. You are going to get my short answer. > > > > Also, in statistical science, it seems that most people who think they > > have a reasonably smart, or at least sensible, answer think some of > the > > other guys' reasonably smart answers are really fairly stupid, or at > > least difficult to understand. So it may be colourful if and when people > > start telling me that after a few decades of sweat and toil I _still_ > > don't understand statistics at all. > > > > If the question is what meaning is attached to a P-value, then there > > seem many possible partial answers. > > > > 1. I am looking only at a sample of size n and I think of this as only > > one of many possible samples of the same size from a larger population. > > That is most plausible if someone really did select that sample using > > random numbers, or something equivalent, and it's a greater or lesser > > stretch otherwise. In many cases the sample you have just fell into > your > > lap somehow > > and the whole exercise is to treat the data _as if_ it were a random > > sample, partly because that's a calculation you can do. There's usually > > some wishful thinking involved. Both texts and teachers vary enormously > > on how candidly they discuss what is going on. This seems to be what > is > > most emphasised in most introductory courses and texts, but it may be > > the least applicable story in statistical practice! > > > > 2. I am looking at a sample of size n and I am willing to think of this > > as one possible outcome among many. I can get a reference population > by > > resampling the data I have repeatedly. Permutation and bootstrap methods > > fit under this heading. I think it wry that in less than 30 years > > bootstrap methods have gone from being widely regarded as a form of > > cheating to being widely considered as the best way to get a P-value > in > > many problems. > > > > 3. I have a model, at its simplest response a function of predictors > > plus some error term, and the uncertainty comes from the fact that the > > model is always a approximation and stochastic by virtue of its error > > term. Whether your n is the whole N is immaterial, because the > > uncertainty is not about sampling at all. > > > > 4. What I have I regard as the realisation of a stochastic process > > (usually in time, or space, or both). The realisation is unique, but > at > > least in principle there could have been other realisations. > > > > I won't quarrel with anyone who thinks #3 and #4 sound the same. > > > > 5. Bayesians have other stories. > > > > 6. I must have forgotten or be unaware of yet other stories. Bill Gould > > has tried to explain quantum mechanics to me several times. I am pretty > > clear that he understands it very well. > > > > In these terms you seem to be saying #1 does not apply in your case, > but > > > > that still leaves other arguments, and there is a lot of scope for > > arguing what is central to #1 in any case. > > > > Nick > > [email protected] > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Re: RE: when your sample is the entire population***From:*"Michael Blasnik" <[email protected]>

**References**:**st: when your sample is the entire population***From:*Lloyd Dumont <[email protected]>

**st: RE: when your sample is the entire population***From:*"Nick Cox" <[email protected]>

**st: Re: RE: when your sample is the entire population***From:*"Michael Blasnik" <[email protected]>

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