Common Errors in Statistics (and How to Avoid Them)

About this book

The Fourth Edition of this tried-and-true book elaborates on many key topics such as epidemiological studies, distribution of data; baseline data incorporation; case control studies; simulations; statistical theory publication; biplots; instrumental variables; ecological regression; result reporting, survival analysis; etc. Including new modifications and figures, the book also covers such topics as research plan creation; data collection; hypothesis formulation and testing; coefficient estimates; sample size specifications; assumption checking; p-values interpretations and confidence intervals; counts and correlated data; model building and testing; Bayes' Theorem; bootstrap and permutation tests; and more.

Contents

Preface xi

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

Glossary 287
Bibliography 291
Author Index 319
Subject Index 329

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Biography

Phillip I. Good, PhD, is Operations Manager at Information Research, a consulting firm specializing in statistical solutions for private and public organizations. He has published more than thirty scholarly works and more than 600 popular articles. Dr. Good is the author of Introduction to Statistics Through Resampling Methods and R/S-PLUS(r), Introduction to Statistics Through Resampling Methods and Microsoft Office Excel(r), and Analyzing the Large Number of Variables in Biomedical and Satellite Imagery, all published by Wiley.

James W. Hardin, PhD, is Associate Professor and Biostatistics Division Director of the Department of Epidemiology and Biostatistics at the University of South Carolina. Dr. Hardin has published extensively in his areas of research interest, which include generalized linear models, generalized estimating equations, survival models, and computational statistics. He is also an affiliate faculty member of the Institute for Families in Society at the University of South Carolina.