There is an old debate going back to the 1970s about the meaningfulness of statistics when dealing with entire populations. If I recall correctly, Judith Tanner edited a book of papers on the subject. Proponents of testing in this circumstance say that we can think of the population of countries as having been sampled from an imaginary larger population of all possible countries, but I think this is ridiculous. We know that the existing set of countries was generated by historical processes (conquest, secessions, and the like) that wasn't random. With time series data it may make sense to imagine a hypothetical random generating process from which a certain stretch of time has been sampled. Ther may also be circumstances where something like bootstrap standard errors could be informative. Suppose you are studying all the children in a school. You would not have a simple random sample, but might still want to know how sensitive your results are to the possibility that a few ch
ildren were not there on the day you passed out your survey instrument because they were sick or truant. David Greenberg, Sociology Department, New York University
----- Original Message -----
From: Daniel Wilde <[email protected]>
Date: Friday, January 18, 2008 1:25 pm
Subject: Re: st: when your sample is the entire population
To: [email protected]
> Hi,
>
> I can't answer this. But I would like to make an additional point.
> Surely
> this has always been commonplace in macro-data. When you do a
> cross-country
> regression of economic growth and investment for example, you have
> nearly
> every country, but all the papers I've seen still report the standard
>
> statistics (p-values etc) Why is this? Are they inferring that we have
>
> sampled all existing countries from a population of all the countries
> that
> could have potentially existed? I'm not sure I'm that I'm convinced by
> such
> logic.
>
> Thanks
>
> Daniel
>
> --On 18 January 2008 09:59 -0800 Lloyd Dumont <[email protected]>
> wrote:
>
> > Hello, everyone. I am facing a statistical
> > "challenge" that must be commonplace as microdata
> > becomes more and more accessible. I have been
> > estimating models using xtreg, as I have people
> > coming and going monthly over about a two year period.
> > Some estimates significant, others not.
> >
> > But, if the people in the "sample" are the entire
> > population that I am inferring to, conventional
> > measures of significance seem inapprpriate. But, I
> > have never read any social science that presents
> > regression estimates, and then says something along
> > the lines of, "These are what they are. Significance
> > doesn't apply here."
> >
> > So...
> > -Am I roughly correct?
> > -Is there some other measure of "certainty" that might
> > be informative in these situations?
> > -Is there a name for this type of "sample" or
> > estimation issue? Googling it has been a real
> > challenge, though there must be lots of
> > writing/commenting on this matter.
> >
> > Thanks for your thoughts. Lloyd Dumont
> >
> >
> >
> >
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