# Basic Statistics: Understanding Conventional Methods and Modern Insights

## Synopsis

This introductory statistics textbook for non-statisticians covers basic principles, concepts, and methods routinely used in applied research. What sets this text apart is the incorporation of the many advances and insights from the last half century when explaining basic principles. These advances provide a foundation for vastly improving our ability to detect and describe differences among groups and associations among variables and provide a deeper and more accurate sense of when basic methods perform well and when they fail. Assuming no prior training, Wilcox introduces students to basic principles and concepts in a simple manner that makes these advances and insights, as well as standard ideas and methods, easy to understand and appreciate.

## Excerpt

There are two main goals in this book. The first is to describe and illustrate basic statistical principles and concepts, typically covered in a one-semester course, in a simple and relatively concise manner. Technical and mathematical details are kept to a minimum. Throughout, examples from a wide range of situations are used to describe, motivate, and illustrate basic techniques. Various conceptual issues are discussed at length with the goal of providing a foundation for understanding not only what statistical methods tell us, but also what they do not tell us. That is, the goal is to provide a foundation for avoiding conclusions that are unreasonable based on the analysis that was done.

The second general goal is to explain basic principles and techniques in a manner that takes into account three major insights that have occurred during the last halfcentury. Currently, the standard approach to an introductory course is to ignore these insights and focus on methods that were developed prior to the year 1960. However, these insights have tremendous implications regarding basic principles and techniques, and so a simple description and explanation seems warranted. Put simply, when comparing groups of individuals, methods routinely taught in an introductory course appear to perform well over a fairly broad range of situations when the groups under study do not differ in any manner. But when groups differ, there are general conditions where they are highly unsatisfactory in terms of both detecting and describing any differences that might exist. In a similar manner, when studying how two or more variables are related, routinely taught methods perform well when no association exists. When there is an association, they might continue to perform well, but under general conditions, this is not the case. Currently, the typical introductory text ignores these insights or does not explain them sufficiently for the reader to understand and appreciate their practical significance. There are many modern methods aimed at correcting practical problems associated with classic techniques, most of which go well beyond the scope of this book. But a few of the simpler methods are covered with the goal of fostering modern technology. Although most modern methods cannot be covered here, this book takes the view that it is important to provide a foundation for understanding common misconceptions and weaknesses, associated with routinely used methods, which have been pointed out in literally hundreds of journal articles during the last half-century, but which are currently relatively unknown among most non-statisticians. Put another way, a major goal is to provide the student with a foundation for understanding and appreciating what modern technology has to offer.

The following helps illustrate the motivation for this book. Conventional wisdom has long held that with a sample of 40 or more observations, it can be assumed that . . .

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