Re: Collinearity, confidence intervals and sampling


"Richard Ulrich" <Rich.Ulrich@xxxxxxxxxxx> wrote in message news:5o0s24p0al837j4esq82vm9bs0os8slj1m@xxxxxxxxxx
On Fri, 16 May 2008 09:54:15 +0100, "reflex" <sdfs@xxxxxxxxx> wrote:


Say if you had a population sample of all hospitals in England, and you
wanted to say something interesting about all hospitals in England, then you
wouldn't need to generalise to a wider population because you know the whole
population. Surely that's a real application?

What can you say about "all the hospitals in England"
in the year 2007 that *remains interesting to people* , if
you are unable to extrapolate or infer something about
the hospitals in the year 2008?
- or any other hospitals in any time or place....

Come up with something *interesting*, and I think I
can show you that you are drawing inferences.


--
Rich Ulrich
http://www.pitt.edu/~wpilib/index.html

The population of interest is what you are trying to generalise to in the first place though isn't it? So in taking a random sample from a set population (hospitals in England) you can arguably generalise to all hospitals (assuming you sample is good/representative enough). And if you have data on the whole population, no generalising is needed, and hence this is a good thing. You *are* inferring about all hospitals in the future of course, but aren't you doing this with all research? You can't sample the future, yet! So it is preferable to have population-level data and draw inferences about the future than have a random sample of that population and draw inferences about the future.

.

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