1. Why Learn Statistics? -- 2. Describing Data -- 3. Probability -- 4. Discrete Random Variables -- 5. Continuous Random Variables -- 6. Sampling Distributions -- 7. Hypothesis Testing and Confidence Intervals -- 8. Comparing Two Independent Groups -- 9. Anova: Comparing Two or More Independent Groups -- 10. Two-Way Designs -- 11. Comparing Dependent Groups -- 12. Multiple Comparisons -- 13. Correlation and Regression -- 14. Categorical Data -- 15. Methods Based on Ranks.

This book not only provides a simple description of the basic concepts and principles of statistical analysis for the social sciences, but also points to failures of methods and offers ways to correct such problems. For example, it was once thought that standard hypothesis testing procedures for means have relatively high power under nonnormality. Many studies have since demonstrated that power can be very low even with very slight departures from normality. This book explains how and why such departures can occur. Statistics for the Social Sciences goes beyond the typical introductory material on probability and statistical inference to incorporate modern topics of critical importance in social science research. The text includes coverage of such topics as robust methods for comparing measures of location; modern regression techniques: recent developments associated with heteroscedastic procedures; the latest developments in nonparametric statistics; and a full chapter on multiple comparison procedures. The accompanying disk contains more than 150 easy-to-use Minitab macros that provide the student or researcher with state-of-the-art statistical methods not available in standard statistical packages. Once the data are read into Minitab, a single command provides access to more powerful, flexible, and accurate statistical procedures.