This text provides a thorough introduction to the statistical methods used in the experimental sciences and to the numerical methods used to implement them. Bridging the gap between statistical theory and practical problems, the treatment emphasizes concise but rigorous mathematics while retaining its focus on applications. After introducing probability and random variables, the book turns to the generation of random numbers (and application to Monte Carlo methods) and to important distributions (such as the binomial, Poisson, and normal distributions). Subsequent chapters discuss statistical samples, the maximum likelihood method, and the testing of statistical hypotheses. The text concludes with a detailed discussion of several important statistical methods : least squares, minimization, analysis of variance, polynomial regression, and analysis of time series. Appendices present the necessary methods of matrix algebra and combinatorics as well as many useful formulae, algorithms, and computer routines. The reader is presumed to have a sound basic knowledge of differential and integral calculus and some knowledge of vectors and matrices. The richly illustrated text is intended for graduate students setting out on experimental research, but it should also provide a useful reference and programming guide for scientists and professionals. To guide the student, the text includes many worked-out examples as well as problems (many with hints or solutions). An accompanying CD-ROM (for IBM PS/2 or PC machines) provides an extensive source program library (in Fortran 77 and in C) to be used as a tool kit for the reader's own applications, a graphics library (for DOS, Windows 951, and Linux), and many sample programs (as source code and executable files).