Robust statistical methods excel across various disciplines by maintaining performance under standard assumptions while also thriving in challenging conditions where traditional techniques falter. This adaptability makes them a valuable tool for researchers and practitioners seeking reliable results in diverse scenarios.
Substantially Improving Power and Accuracy
Conventional statistical methods often overlook critical differences and associations that modern techniques can reveal, particularly in cases of slight deviations from normality. While numerous articles highlight the limitations of standard approaches, clear and straightforward explanations are scarce. The book addresses scenarios where results deemed nonsignificant by traditional methods may turn significant through advanced analysis, emphasizing the importance of adopting modern statistical techniques for more accurate insights.
Applying Contemporary Statistical Techniques explains why traditional statistical methods are often inadequate or outdated when applied to modern problems. Wilcox demonstrates how new and more powerful techniques address these problems far more effectively, making these modern robust methods understandable, practical, and easily accessible.* Assumes no previous training in statistics * Explains how and why modern statistical methods provide more accurate results than conventional methods* Covers the latest developments on multiple comparisons * Includes recent advances in risk-based methods * Features many illustrations and examples using data from real studies * Describes and illustrates easy-to-use s-plus functions for applying cutting-edge techniques * Covers many contemporary ANOVA (analysis of variance) and regression methods not found in other books
Conventional statistical methods routinely miss differences among groups or associations among variables. These differences are detected by more modern techniques. Hundreds of journal articles have described the reasons why standard techniques are unsatisfactory. Nonetheless, simple and intuitive explanations are generally unavailable. Without assuming any prior training in statistics, Part I of this book describes basic statistical principles from a point of view that makes their shortcomings easy to understand. Part II describes modern methods that address the problems covered in Part I. Using data from actual studies, many examples are included.