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Cancer deficiency clusters
This reason is exactly why statistics profs advise
people doing _research_ to use 2-sided confidence
limits, and discourage the practice of 1-sided limits
in most cases. [1-sided limits have some valid
applications in non-research fields such as quality
control and business management].
Along with this recommendation came the recommendation
not only to look for what one 'thinks might happen' in
an experiment, but to look for _anything_ that's
happening.
Fortunately, the NSWS used 2-sided limits, although
there are studies by less competent epidememiologists
that used 1-sided limits (improperly IMHO).
~Ruth 2
--- Ted Rockwell <tedrock@CPCUG.ORG> wrote:
> > That's what I've been taught in "Research
> Methodology" cpurse few years
> ago.
>
> Let me get this straight, guys. Suppose I read
> about a restaurant that is
> decorated in blue. And I think, "They've got it
> wrong. I've been there.
> It's red." So I go back with the research intention
> of proving that the
> restaurant is red. And I find it's blue. I take
> pictures and they come out
> blue, But that doesn't proved anything, because if
> my INTENTION was to
> prove it red, then my photos can only "suggest" that
> it's blue.
>
> C'mon. Does anyone really believe that? I'm
> willing to admit that an
> experiment designed to prove one thing might not be
> optimum to prove
> another. When you're intent on counting dead mice,
> you might not notice the
> happy mice. For example, there's a famous
> experiment which reported "Since
> there is no reason to expect that radiation will
> beneficial, any deviations
> from norm will be assumed to be detrimental." But
> that's just bum science.
>
> What am I missing here?
>
> Ted Rockwell
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