The best preprocessing methods will be the ones that ultimately produce a robust model with the most accurate predictive ability. Unfortunately, there are no particularly straightforward rules to guide investigators to the best selection of preprocessing options; the subsequent trial and error optimization process may be quite time consuming and confusing. However, spending little or no time investigating preprocessing options is likely to result in less than optimal results. The primary objective of this book is to present a relatively focused outline of the major options available for data analysis. The most frequently used methods are noted to varying degrees of elaboration; the more useful methods are discussed in more detail. Although the methods chosen generally reflect the current literature, they also reflect the personal biases of the author and limitations regarding the length of the book. It is not feasible to exhaustively discuss each method.