Fundamentals of Modern Statistical Methods

The book, authored by a professor of psychology with experience in applied statistics, is aimed at non-professional statisticians. The reader is not assumed to have any prior training in statistics. The statistical concepts are explained at an elementary level in a nontechnical way to social scientists, physical scientists, and other applied researchers. Each chapter is closed with a useful summary of key points. The book is divided into two parts and has 12 chapters. The Ž rst part provides a verbal and graphical explanation of standard statistical methods, including outlier detection, conŽ dence intervals, hypothesis testing, and linear regression. The author also discusses why standard methods can be highly misleading at times. Practitioners should be aware that Student’s t-test is meaningful only for normal populations and that it leads to misleading conclusions for non-normal populations. Some of these aspects are discussed in detail in the book. The computer intensive resampling method called bootstrap is brie y discussed here. However, I recommend the book, Bootstrap Methods by C. R. Chernick for this computer intensive method. It is an excellent guide for practitioners and it has an extensive bibliography on bootstrap methods, making it an indispensable reference source. Part 2 of the book, consisting of Ž ve chapters, concentrates on more recent methods, such as robust measures of location (trimmed mean, M -estimators) and their inferences; measures of association (Pearson’s correlation, Kendall’s tau, Spearmans’s rho); robust regression (Theil–Sen estimator, regression based on least absolute distance method, M -estimators, least trimmed squares); and non-parametric methods (Wilcoxon–Mann–Whitney test, permutation tests). The book may give a basic idea of what statistics can offer, for those with little or no exposure to statistics.