The attitrion of taxonomic expertise

James Lyons-Weiler weiler at ERS.UNR.EDU
Fri Oct 18 08:31:45 CDT 1996


On Fri, 18 Oct 1996, Alan Harvey wrote:

>
> Maybe it's just me, but favoring partition A over partition B simply
> because you applied a statistical test to A but not B seems to violate the
> most basic premises of statistical testing.  In any case, _my_ point was
> that statistical methods came into being because more direct ways of
> analyzing the results of molecular data sets were unavailable.
> Morphologists seldom use these statistical methods because they prefer the
> direct approach, and because of concerns about the statistics themselves,
> which, for example, seem to address questions that, in practice, are seldom
> relevant to morphological data sets (e.g., "Is there a significant
> phylogenetic signal in this data set?"). In this view, the mere
> availability of Statistics does not automatically confer superiority to one
> kind of data.
>

I could not agree more about the fallacyof accepting partition A
ober B simply because one has been analyzed; that, in fact, is my
point.  I fail to see, however, why morphology can or should be excused
from statistical tests that are now available.

> Circularity arguments are logical landmines. I for one am skeptical of the
> argument that "because you use the characters to build the tree, you can't
> use the tree to study characters." And in any case, I described using the
> tree to identify characters that should be re-evaluated, not to do the
> actual evaluation. There are lots of interesting issues here!

To identify a character state as a synapomorphic because it is not
homoplastic on the tree it helped you choose is entirely circular.  What
the statistical test I described avoids is relying on that circularity.
It does this by measuring a predictable consequence of the presence of
phylogenetic information in a matrix without using trees at all.  It
does all of this with one of the simplest of tests (student's t-test
for two means). This is helpful because cladistic parsimony will give you
a set of optimal trees even for random data, and prevents the application
of spurious, non-informative data.  I hope it's found to be helpful.

James Lyons-Weiler

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