Ok as I see it, the issue at hand is to transform guano into fertilizer...
(poor data into useful information)
Agree most likely our data will have particular biases and errors incorporated
into it.
Two questions surface in my mind.
- "What manipulations and processes can I perform to eliminate the biases and
errors?"
-"What steps are required to get more overall precision and reliability without
increasing the precision of the individual components?"
If we can construct tools to provide higher precision than the components used
and eliminate the flaws contributed by individual components in mechanical
systems, why can't we create useful appraisal processes given the existing flaws
of the measurement tools?
Saludos
Esteban
-------------------------------------
a veneer
I think if we were to talk about this further we'd have to go into all
kinds of things that are rather esoteric, like measurement error,
and so forth, which also have statistical components.
The NBA example was, as you said, an easy one, and purer to
illustrate the fallacy of normal distributions. But the principle, as far
as I can figure is the same. In fact it's moreso.
If you have an unreliable measurement instrument, or one of
questionable validity, ANY use of math or statistics is going to give
you bad results, GIGO. Which is why, in fact it is virtually pointless
to mathematically or statistically manipulate (operate on) appraisal
data, unless it is measures more precisely.
In a sense though the issue in practice wouldn't be the normality of
the curve (which most of us mortals aren't going to have the
resources to test in real life(, but whether ANY mathematical
functions should be used on poor data.
So, you have two issues. Can we assume a normal distribution
without verfying (No) and Is our data solid enough to use statistical
functions on them (often no).
...but the reason people continue to do that and pretend the data is
good is that it provides a veneer of scientificiousity to the process,
which of course employees recognize as complete guano.
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