Thom Baguley wrote:
> This allows us to generalize to a hypothetical population of
> similar data sets
This is a tricky statement, with more to it than meets the eye. On the
one hand, we do usually generalize to a population of *individuals*
rather than of *sets*. On the other hand, the observation generalizes
not to the individuals in the real world, but to observations of them -
data. And there are some types of observation effect (learning effects,
fatigue, etc) that are evident only in the set, and cannot be observed
in one datum.
(it is a misconception to think that a specific population -
> such all students or all students in Kentucky, or all students wearing blazers
> - is being generalized to).
Again, there is a lot in this, but perhaps it would be more accurate to
say that the process of generalizing to a wider population is
extrastatistical, and separate from the statistical process of
generalizing to which Thom alludes. If we really believed Thom's words
literally, the literature would be full of papers entitled "Effects of
Fatigue on the Reaction Times of Students in the Psych 100 Subject Pool
at Euphoric State U. (Class 0f '05) On Sept. 30, 2003" and, given its
irrelevance to anybody else, nobody would sponsor such research except
the subjects' parents. Even physics journals would have papers like
"Abrasion by Airborne Particles of Pieces From One Slab of Styrene That
My Lab Assistant Lost Last Week So, No, You Can't See It." Experimental
science would end up, like Borges' character "Funes the Memorious",
bewildered by a cloud of unrelated minutiae.
To make any use of these results, we have to decide what populations
they *do* apply to. We have to realize that it's *not* necessarily what
we'd like; we have to realize that the stats won't do it for us, though
common sense may; and we have to accept that the bigger the population
the less the application.
-Robert Dawson
.
.
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