Positivism vs Realism
James Francis Lyons-Weiler
weiler at ERS.UNR.EDU
Mon Dec 15 10:25:57 CST 1997
On Mon, 15 Dec 1997, Tom DiBenedetto wrote:
> > No, I haven't - because I don't know what the result would
> > mean.
> I'll tell you what it would mean. It would provide an indication of
> whether morphological datasets are ever, or very often of
> questionable significance (by your standards); a question that you
> seem constantly to raise. You ask me if I would put forward a tree
> which is judged to be indistinguisahble from one generated by random
> data; as if this were a pressing concern. And I would like to know
> how often you have found it to be a real concern - in morphological
> studies. As you know, morphological studies are done on datasets
> which represent many years of careful study; on homology hypotheses
> which are well tested in the biological realm. As you also know, many
> morphological systematists tend to see these "tests against
> randomness" to be a little besides the point. Now for molecular
> sequences, where there is not much, beyond alignment, to be
> studied,,there is a general sense that perhaps these tests have
> meaning. I think these are very interesting questions.
I recall that randomization tests for signal revealed
that most morphological data sets had signal - and that
the explanation offered was that morphologists
were doing a good job. There are some data sets
where the randomization tests pass a particular
morphological matrix on the yes/no question of
significant hierarchical character covariation -
but for which some tree-independent tests report
distinct sources of a pathological (misleading)
incongruence. Nevertheless, if morphologists
accept your view that their data are worth
"testing" by parsimony, it is perplexing why
they should eschew tests based on significance
testing. If they have done their job, then
there data will pass - and the more critical
tests their data pass, the more confident they can
be that the data won't mislead phylogenetic inferences.
Funny you should mention alignments, T. I'm moving
on to that question...
> > The test is designed for the specific case, one
> > matrix at a time.
> so what? I am just wondering what proportion of datasets, considered
> one at a time, are found to be presenting insignificant results.
What would that tell us, really? What if morphological
data sets were found to be generally wanting, the reaction
would be "who needs this new stuff?" rather than "oh -
ok, now I've learned something more about my data than
I knew previously..."
If morphological data sets were almost always found to
be pathology-free, then the reaction would be "we're
doing fine - who needs this new stuff?". My point is such a study
wouldn't necessarily be informative about the next data set
constructed, or the next, or the next... That's the type of
induction Popper fought so hard against, and I rarely
lend any weight to unqualified generalizations. For instance,
the t-test requires that a certain distributional assumption
apply (normality) - but it is also appreciated that the test
tends to be robust to such violations. Analysts nevertheless
insist that the assumption be tested before hand, because
the violation can be problematic. So the general statement
"the t-test is robust to violations of the normality
assumption" is not a license to ignore that assumption in
any particular case.
> > I have found published morphological
> > data sets for which the test reveals sources of incongruence,
> > including long edges, but I don't find the number of
> > instances a very interesting question. It could be high,
> > or low.
> Why not?? It speaks to the efficacy of the procedures. Something you
> certinly seem interested in discussing a lot.
Which procedures? Collecting data? But see my point about
> > A question you didn't ask that I find more
> > interesting is how many morphological data sets can I find
> > for which the application of the tests improves the
> > degree of congruence by pinpointing sources of noise -
> > but that work is underway.
> Improve the degree of congruence by pinpointing noise - I wonder what
> that means, other than what parsimony does. Parsimony of course,
> finds congruence. Noise is incongruence. You are imposing some new
> standard here, right? You are finding a reason to dismiss character
> matches even if they are congruent (i.e. even if they are retained by
> parsimony - else how would you be doing anything different).
> Interesting,,,,,,what if they are real though...?
It's not dismissive - it's pointing to characteristics and
interactions among the distribution of states among characters
(relative to that which can be expected by chance) that bear
a more critical look than that provided by the indication
of congruence on the parsimony tree. In short, it throws
up a red flag to those apparently robust hypotheses of homology
that require further investigation - and it also sends out
a warning that the hypotheses of homology involved cannot
be trusted to behave themselves in the exercise of
tree-based inquisitiions of congruence.
> > We can't expect all regimes of character
> > and taxon sampling to yield matrices that are not misleading.
> Nor can we expect statistical regularities to inform us when
> particular data points are misleading,,,,,right?
Pardon? If your are asking whether statistical methods have
limitations, the answer is a very loud yes - the first
principle of methods of inference is that ALL methods
of inference have limitations. Most of the time, when
they are made explicit, then they can be turned to great
advantage when appraoched with a skeptical mind. Hence
the recent move to test for cases when the assumptions of
phylogenetic inference (leading to the standard bifurcating
tree) are (apparently) violated. IMHO, fields can stagnate
when their methods of inference are accepted uncritically
and their limitations are unrecognized or denied. An
example of this is Popper's warning against misplaced
faith in formalism and convention. But limitations are rarely
fatal, especially when they lead to improvements.
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