Despite widespread use, the bootstrap remains a controversial method for assessing confidence limits in phylogenies. Opposition to its use has centered on a small set of basic philosophical and statistical objections that have largely gone unanswered by advocates of statistical approaches to phylogeny reconstruction. The level of generality of these objections varies greatly, however. Some of the objections are merely technical, involving problems that are found in almost all statistical tests, such as bias in small data sets. Other objections are really associated not so much with a rejection of the bootstrap but with the rejection of statistical methods in phylogeny reconstruction, which resurrects an old debate. The most relevant aspects of this debate revolve around the issue of whether or not an unknown parameter, such as a tree, can have probabilities (confidence limits) associated with it. The relevant statistical aspects are reviewed, but because this issue remains controversial within statistical theory, it is unreasonable to expect it to be anything else in phylogenetic systematics. An area of common ground between statistical and nonstatistical approaches emerges in the use of statistical likelihood as a measure of support for phylogenetic hypotheses. This common ground requires the abandonment of classical notions of confidence limits by statistically oriented systematists and the acceptance of probabilistic models and likelihood by opponents of statistical methods. There remains a small set of objections directly germane to bootstrapping phylogenies per se. These objections involve issues of random sampling and whether or not character data are independent and identically distributed (IID). Nonrandomsample bootstrapping is discussed, as are sample designs that impose the IID assumption on characters regardless of evolutionary nonindependence and nonidentical distribution of those data. Systematists wishing to use the bootstrap have an alternative to making explicit and rather strong evolutionary assumptions; they can consider the issue of character sampling designs much more carefully.
- Statistical inference
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics