In my work, we are comparing predicted rankings versus true rankings for some sets of data. Up until recently, we've been using Kendall-Tau alone. A group working on a similar project suggested we try to use the [Goodman-Kruskal Gamma test](http://en.wikipedia.org/wiki/Gamma_test_(statistics)) instead, and that they preferred it. I was wondering what the differences between the different rank correlation algorithms were. The best I've found was [this answer](http://stats.stackexchange.com/questions/3943/kendall-tau-or-spearmans-rho/3946#3946), which claims Spearman is used in place of usual linear correlations, and that Kendall-Tau is less direct and more closely resembles Goodman-Kruskal Gamma. The data I'm working with doesn't seem to have any obvious linear correlations, and the data is heavily skewed and non-normal. Also, Spearman generally reports higher correlation than Kendall-Tau for our data, and I was wondering what that says about the data specifically. I'm not a statistician, so some of the papers I'm reading on these things just seem like jargon to me, sorry.