Statistics is one of the most practical and essential courses that you will take, and a primary goal of this popular text is to make the task of learning statistics as simple as possible. Straightforward instruction, built-in learning aids, and real-world examples have made STATISTICS FOR THE BEHAVIORAL SCIENCES, 10th Edition the text selected most often by instructors for their students in the behavioral and social sciences. The authors provide a conceptual context that makes it easier to learn formulas and procedures, explaining why procedures were developed and when they should be used. This text will also instill the basic principles of objectivity and logic that are essential for science and valuable in everyday life, making it a useful reference long after you complete the course.
- This edition's excellent problems, which walk students step-by-step through procedures in a variety of ways, reflect the straightforward writing for which Gravetter and Wallnau are renowned. Each chapter ends with 20 to 30 problems.
- In-text learning aids give students many opportunities to master the material by practicing working through problems. A Focus on Problem Solving section in each chapter offers practical tips on deciding which formulas to use and cautions for avoiding common errors.
- The book's conceptual context helps make learning statistics as simple as possible for students who may be intimidated by the subject. Statistical formulas are presented in both standard mathematical notation and in everyday language, with explanations of how and why formulas are used.
- In the Literature sections, which appear in nearly every chapter, demonstrate how statistical results (e.g., for the mean and the standard deviation, t tests, and independent-measures tests) are reported in APA style and explain the notation and jargon used.
- An appendix contains a general introduction to IBM SPSS®. In addition, at the end of each chapter for which an SPSS analysis is feasible, a step-by-step set of instructions describes how to enter data and run the analysis, and presents an example of the output.
- Each section of every chapter begins with a list of Learning Objectives and ends with a Learning Check consisting of multiple-choice questions -- with at least one question related to each Learning Objective. Do-It-Yourself examples present students with an opportunity to test their understanding by solving a computation problem related to the current topic (a final answer is provided).
- The previous edition's Chapter 19, outlining how to identify the correct statistical procedures for specific data sets, has been moved to the Appendix as a Statistics Organizer.
1. Introduction to Statistics.
2. Frequency Distributions.
3. Central Tendency.
5. z-Scores: Location of Scores and Standardized Distributions.
7. Probability and Samples: The Distribution of Sample Means.
8. Introduction to Hypothesis Testing.
9. Introduction to the t Statistic.
10. The t Test for Two Independent Samples.
11. The t Test for Two Related Samples.
12. Introduction to Analysis of Variance.
13. Repeated-Measures Analysis of Variance (ANOVA).
14. Two-Factor Analysis of Variance (Independent Measures).
16. Introduction to Regression.
17. The Chi-Square Statistic: Tests for Goodness of Fit and Independence.
18. The Binomial Test.