I can think of 2 better methods:
1. Bayes. Sample from the set of 100 trees, used as a prior on the
"true" tree structure (assuming they are a true posterior set, as Joe
points out). See: de Villemereuil et al. BMC Evolutionary Biology 2012,
12:102 http://www.biomedcentral.com/1471-2148/12/102
If the 100 trees are trees sampled from a Bayesian posterior, or else trees
from bootstrap samples of your data, then you might just take the estimates
from each tree (say estimates of a regression coefficient). Consider their
distribution and ask whether the null hypothesis value (such as having
Alternatively, the proportion of trees that gave in a significant result
(for a given threshold) could be of interest. It depends on your question.
Simon
Simon Joly, Ph.D.
Chercheur, Jardin bo
Darrin, list-
I'm sure there's people on this list with better answers, so I'll
throw in first with what might be the wrong answer (but feels right to
me), and say you more or less need to report all of them: like, show a
full histogram of the p-values. At least, as a reviewer, that is what
would
I am running a series of statistics on a subset of 100 trees that returns 100
different p-values. I was wondering what the best way to report summary
statistics for these 100 p-values would be (median?, measure of variance in all
100 p-values?). Thanks for any insight.
