Preface This book focuses on the practical aspects of modern and robust statistical methods. The increased accuracy and power of modern methods, versus conventional approaches to the analysis of variance (ANOVA) and regression, is remarkable. Through a combination of theoretical developments, improved and more flexible statistical methods, and the power of the computer, it is now possible to address problems with standard methods that seemed insurmountable only a few years ago. The most common approach when comparing two or more groups is to compare means, assuming that observations have normal distributions. When comparing independent groups, it is further assumed that distributions have a common variance. Conventional wisdom is that these standard ANOVA methods are robust to violations of assumptions. This view is based in large part on studies, published before the year 1960, showing that if groups do not differ (meaning that they have identical distributions), then good control over the probability of a type I error is achieved. However, if groups differ, hundreds of more recent journal articles have described serious practical problems with standard techniques and how these problems might be addressed. One concern is that the sample mean can have a relatively large standard