At 10:23 AM 4/13/00 -0500, Michael Granaas wrote:
>In addition to defining the variables some areas do a better job of
>defining and therefore testing their models. The ag example is one where
>not only the variables are relatively clear so are the models. That is
>there is one highly plausible reason for rejecting a null that fertilizer
>does not effect crop production: Fertilizer increases crop production.
>You have rejected a model of no effect in favor of a model positing an
>effect.
i did not know that ag research ... in this case, production figures ...
was so easily accomplished ...
it might be relatively easy to distribute fertilizer in different amounts
... over plots ... but even there, there is considerable error ... check
out the way our spreaders work on our lawns? and in addition ... every
fertilizer i know of is a product that is an amalgamation of several
subproducts ... and inert stuff too ... so the distribution of it over
plots will not produce identical spreads ...
and ... how is production measured? to compare across plots means gathering
in crops ... and making some kind of 'volume' measurements ... and that
seems much easier said than done
now, i would not like to say that doing a fertilizer experiment has the
same amount of 'error' as maybe one where we ask if different levels of
intelligence impact differentially on problem success in later life ... but
these differences are more a matter of degree ... than in one instance it
is easy ... and in others it is not
maybe we should ask the ag researchers if THEY think doing their research
is simple
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