"Numerical Recipes" is the name of a series of books oft cited and frequently cursed by computer programmers the world over.

The purpose of this set of books is to be a reference for statistical and numerical algorithms used commonly in data proccessing and scientific computing. It covers things like:

As well as a host of other common problems, and the occasional geek-oriented commentary on the world. I quote from the introduction to the chapter on statistical description of data (italics are theirs) chosen to show the entertaining and whimsical nature of some of the accompanying text:

"If a statistic falls in a *reasonable* part of the distribution, you must not make the mistake of concluding that the null hypothesis is 'verified' or 'proved.' That is the curse of statistics, that it can never prove things, only disprove them! At best, you can substantiate a hypothesis by ruling out, statistically, a whole long list of competing hypotheses, every one that has ever been proposed. After a while your adversaries and competitors will give up trying to think of alternative hypotheses, or else they will grow old and die, and *then your hypothesis will become accepted*. Sounds crazy, we know, but that's how science works!"

The books take the form of describing an algorithm and then showing a sample implementation. The descriptions are brief and a little dense, but very helpful because they are a good starting point to go look for more information. The example code, however (at least in the C version of the book) is next to useless for a variety of reasons (including the fact that because the code is essentially just translated from FORTRAN all the arrays are treated as 1 based). That being said, the practical and concise description of the methods still make the book more than worth it.

The one I have handy is "Numerical Recipes in C (2nd Edition)", it's written by William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery and publised by Cambridge University Press.