New Lucy: morphology vs. molecular data

[Richard.Zander at MOBOT.ORG]

There are a number of standard statistical tests of independence of data.
But, like null hypothesis testing, if you cannot demonstrate independence of
data (at some particular level) that you cannot does not mean the data are
dependent. That requires a lot of further empirical testing to eliminate
alternative causal explanations.

There may not be enough data to determine probable independence of
morphological traits, and experience guided by intelligence may have to
suffice to identify characters that apparently respond to selection pressure
as a block. Identification of apparent reduction series comes to mind as a
reasonable way to identify nonindependent traits through theory.

Of course, one always tries to clean one's data set of the logical
dependencies, for instance when an organ varies in general size (say,
volume), a measurement of one dimension is sufficient, since using both
length and width weight the trait too high.

Steve Manning wrote:

To get around relying on either of the above assumptions, what do you think
is the best way of evaluating (calculating) the probability that any two or
more traits are "independent"?



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Richard H. Zander
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