Praise for Common Errors in Statistics (and How to Avoid Them)
“A very engaging and valuable book for all who use statistics in any setting.”
—CHOICE
“Addresses popular mistakes often made in data collection and provides an indispensable guide to accurate statistical analysis and reporting. The authors’ emphasis on careful practice, combined with a focus on the development of solutions, reveals the true value of statistics when applied correctly in any area of research.”
—MAA Reviews
Common Errors in Statistics (and How to Avoid Them), Fourth Edition provides a mathematically rigorous, yet readily accessible foundation in statistics for experienced readers as well as students learning to design and complete experiments, surveys, and clinical trials.
Providing a consistent level of coherency throughout, the highly readable Fourth Edition focuses on debunking popular myths, analyzing common mistakes, and instructing readers on how to choose the appropriate statistical technique to address their specific task. The authors begin with an introduction to the main sources of error and provide techniques for avoiding them. Subsequent chapters outline key methods and practices for accurate analysis, reporting, and model building. The Fourth Edition features newly added topics, including:
The book concludes with a glossary that outlines key terms, and an extensive bibliography with several hundred citations directing readers to resources for further study.
Presented in an easy-to-follow style, Common Errors in Statistics, Fourth Edition is an excellent book for students and professionals in industry, government, medicine, and the social sciences.
PART I FOUNDATIONS 1
1. Sources of Error 3
2. Hypotheses: The Why of Your Research 15
3. Collecting Data 31
PART II STATISTICAL ANALYSIS 57
4. Data Quality Assessment 59
5. Estimation 65
6. Testing Hypotheses: Choosing a Test Statistic 79
7. Strengths and Limitations of Some Miscellaneous Statistical Procedures 119
8. Reporting Your Results 139
9. Interpreting Reports 165
10. Graphics 181
PART III BUILDING A MODEL 213
11. Univariate Regression 215
12. Alternate Methods of Regression 237
13. Multivariable Regression 251
14. Modeling Counts and Correlated Data 267
15. Validation 277
