Great blog. I agree, many things are naturally skewed, and always seem to mess up the sample data i'm often asked to analyze.

Just want to mention that a skewed distribution may, in fact, be the reflection of an asymmetric process, one case which may be a convolution of distributions. Keeping the normal distribution as a frame of reference, Tukey suggested modeling the skewed 'part' as a contamination of the normal. This idea was extended into a generalization of our basic continuous family of disributions by Huber, who created a way to downweigh the outlying component as an adjustment of the sampled distribution. So if, in fact, the extreme value(s) are outliers, and not noise functionally related to the mean (so you can transform with logs), you could model them with a generalized estimation procedure called m-estimators. Don't know if you have touched on m-estimation; so please ex-skew me if you have!