Trying to add new stuff to this thread.
The Grubbs and the Chauvernet take the distribution as Gaussian (aca. Normal), I found some time ago (on the NIST site) that there is another way to detect outliers using quartile.
OUTLIERS - NIST
The interesant part is that the use of quartile, work for non Gaussian distributions. Also there is the link to Grubbs method.
I found also in
WAPEDIA Outliers

An Excelent recompliation of methods, and a good reference to Peirce's criterion. And Cook's distance in regression problems, only exclude points which exhibit a large degree of influence on the parameters.