Many, many things aren't a power law, but are widely reported to be due to sloppy statistics and a deep misunderstanding of regression on log-transformed data.

Many, many things aren't a power law, but are widely reported to be due to sloppy statistics and a deep misunderstanding of regression on log-transformed data. The problem is this: people frequently log-transform empirical counts of things (like library book circulation) and then fit a line to the distribution. Since the line generally fits very well, with very high (but ultimately meaningless) R-squared values, they rush off to write a paper. I could cite at least a dozen very "important" papers that gush over power law relationships in this or that. Instead, I'll let Cosma Shalizi's excellent blog post on the topic do the talking and citing: http://cscs.umich.edu/~crshalizi/weblog/491.html http://cscs.umich.edu/~crshalizi/weblog/491.html It's an excellent read for anyone reaching for a log-transform. EDIT: I didn't realize that Cosma was the author of the linked piece. The other blog post by him I link here is also highly recommended.