I've surfed around on this article several times while trying to find a distribution I needed in order to make my rejection samplers work better for distributions that have no name or are obscure (posteriors in Bayesian inference often generate some totally whacky distributions--especially once you get out of the realm of using conjugate priors. Here, there be dragons!).

If you aren't reading probability from a textbook, this article isn't going to tell you which distributions are the most useful. The Beta Distribution receives half the amount of space as the Dirac delta function, but for you folks who want to take a Bayesian approach to A/B testing, the beta is going to be far more useful (The beta is the conjugate prior to the binomial) than the dirac, though one might argue that the Dirac is interesting from a mathematical perspective, it being a degenerate distribution and all...

It turns out though that many of the "useful" distributions (by which I mean the ones that many classical problems were devoted to) share very similar properties. If your aim is to get familiar with the most common distributions encountered in the wild, the article on the exponential family will serve you better than this overwhelming article.

http://en.wikipedia.org/wiki/Exponential_family