Real-World Examples illustrate every concept discussed in the text with detailed, compelling examples from real life. You Try It! exercises follow most worked examples, allowing students to immediately check their understanding by directing them to a similar exercise to solve on their own. Real Data Sources help students see the integration of statistics in everyday life. Neil Weiss has included carefully cited data from reputable journals, newspapers, magazines and websites. Helpful text features help readers make sense of the material and provide easy reference for study and review. Interpretation boxes explain the meaning and significance of statistical results in everyday life and highlight the importance of interpreting answers and results. What Does It Mean? boxes explain in plain English the meaning of definitions, formulas, and key facts. This feature also summarises relevant discussions. Procedure Boxes have been enhanced to include the “why, when, and how” of the statistical methods. Each procedure box has a brief identifying title followed by a statement of its purpose (why it’s used), the assumptions for its use (when it’s used), and the steps for applying the procedure (how it’s used). Technology Appendixes help students use Minitab, Excel, and the TI 83/84 Plus graphing calculators. Parallel Critical-Value/p-Value Approaches offers complete flexibility in the coverage of critical-value and p-value approaches to hypothesis testing. Instructors can focus on one approach or they can compare both approaches. Parallel Presentations of Technology: The Weiss approach offers complete flexibility in the coverage of technology, which includes options for using MINITAB®, Excel®, and the TI-83 and TI-84 Plus graphing calculators. One or more technologies can be explored and compared. Instructions and output for each package are included in Technology Centers throughout the book. Focusing on Data Analysis sections at the end of each chapter examine the Focus Database, a database of 13 variables describing the undergraduate students at the University of Wisconsin—Eau Claire. This feature lets students work with large data sets, practice using technology, and discover the many methods of exploring and analysing data–both descriptively and inferentially.