This book will prepare students to use and understand basic statistical applications and get them ready to move into more advanced statistical studies. Written at a level appropriate for undergraduates, the book gives careful attention to the flow of ideas and concepts so there is a stream of logic internal to each chapter as well as one that links chapters together. Statistical Analysis for the Social Sciences provides a simple to understand but sophisticated introduction to statistics.

The book begins with a discussion of methods for describing the distribution of a variable. The discussion of measures of central tendency and variance is followed by a presentation of continuous and discrete probability distributions. The introduction of probability avoids the traditional discussion of the basic laws of probability in that they are not directly applied in the everyday use of statistical probability. Instead, the discussion is focused on the relationship of probability to outcomes for values in distributions, and the value of probability in statistical decision making.

The discussion of probability is followed by a chapter on the logic underlying statistical inference. The concept of the standard error, and confidence limits link the discussion back to probability. Hypothesis testing is discussed, followed by tests of significance for one sample and two sample tests for various types of statistical measures. Student's t-Test not only illustrates significance testing, but presents an opportunity to introduce the idea of explained variance that is the emphasis of analysis of variance and regression. The discussion of regression and correlation concludes with a demonstration of how correlation analysis provides the basis for evaluating explained variance.