Sorry, I should have used the phrase "inverse cdf". If you can map a pile of data onto 0 to 1 (i.e. by ranking it) then you can invert it onto the values that

Sorry, I should have used the phrase "inverse cdf". If you can map a pile of data onto 0 to 1 (i.e. by ranking it) then you can invert it onto the values that occupy those quantiles for a Gaussian. This is nice because you can use quasi-parametric assumptions (very nice when multi-dimensional relationships are to be explored) without the data itself needing to be distributed appropriately. Other alternatives are to use nonparametric smoothers and the like but I kind of hate going to all that trouble when it's usually pointless. (important exception: when you have interesting correlated structure in high-dimensional data) It's a hack, to be sure, but especially if you want to pool data (e.g. in mixed hierarchical models) for better predictions, it often pays off. The name is a play on "logit", "expit", "probit", "tobit", etc. since the actual transformation is relatively trivial for data that is already sorted. (For large unsorted data or streams, not so much)