Hi, I apologize in advance if this is the wrong approach. All this talk has inspired me to do something about developing the statistics of Scipy. We need to develop a strategy to improve the statistical functions within Scipy. A central requirement is a sort of code review to ensure that the existing functions have adequately documentation (see also the Scipy documentation Marathon http://www.scipy.org/Developer_Zone/DocMarathon2008) and have appropriate tests for functionality and accuracy. Basically I strongly believe that we must carefully use a slow divide-and-conqueror approach to succeed simply due to the scope involved. I would be extremely interested in what people would like to see so that we can develop specific goals and action plans to update and improve them. I consider at the least the following major areas currently present in Scipy that I am aware of: 1) Statistical distributions: Josef has vastly improved this. 2) Uni- and multi-variate kernel density estimation - currently Gaussian only available. 3) Basic statistical functions - available for standard and masked arrays but it is inconsistent. 4) Model fitting aspects that integrates different code within Scipy (including Jonathan Taylor's model class - which is really impressive and the Cookbook ols) to provide important functionality including general linear models, generalized linear models, and generalized additive models. I would suggest that we develop some type of PEP structure as starting point for discussion as well as using different threads to address different areas as well as future directions. Therefore I have put together something to address the basic statistical functions in a separate thread. Thanks Bruce