Common Errors in Statistics (and How to Avoid Them) 4e

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.

Preface xi PART I FOUNDATIONS 1 1. Sources of Error 3 Prescription 4 Fundamental Concepts 5 Surveys and Long-Term Studies 9 Ad-Hoc, Post-Hoc Hypotheses 9 To Learn More 13 2. Hypotheses: The Why of Your Research 15 Prescription 15 What Is a Hypothesis? 16 How Precise Must a Hypothesis Be? 17 Found Data 18 Null or Nil Hypothesis 19 Neyman--Pearson Theory 20 Deduction and Induction 25 Losses 26 Decisions 27 To Learn More 28 3. Collecting Data 31 Preparation 31 Response Variables 32 Determining Sample Size 37 Fundamental Assumptions 46 Experimental Design 47 Four Guidelines 49 Are Experiments Really Necessary? 53 To Learn More 54 PART II STATISTICAL ANALYSIS 57 4. Data Quality Assessment 59 Objectives 60 Review the Sampling Design 60 Data Review 62 To Learn More 63 5. Estimation 65 Prevention 65 Desirable and Not-So-Desirable Estimators 68 Interval Estimates 72 Improved Results 77 Summary 78 To Learn More 78 6. Testing Hypotheses: Choosing a Test Statistic 79 First Steps 80 Test Assumptions 82 Binomial Trials 84 Categorical Data 85 Time-To-Event Data (Survival Analysis) 86 Comparing the Means of Two Sets of Measurements 90 Do Not Let Your Software Do Your Thinking For You 99 Comparing Variances 100 Comparing the Means of K Samples 105 Higher-Order Experimental Designs 108 Inferior Tests 113 Multiple Tests 114 Before You Draw Conclusions 115 Induction 116 Summary 117 To Learn More 117 7. Strengths and Limitations of Some Miscellaneous Statistical Procedures 119 Nonrandom Samples 119 Modern Statistical Methods 120 Bootstrap 121 Bayesian Methodology 123 Meta-Analysis 131 Permutation Tests 135 To Learn More 137 8. Reporting Your Results 139 Fundamentals 139 Descriptive Statistics 144 Ordinal Data 149 Tables 149 Standard Error 151 p -Values 155 Confidence Intervals 156 Recognizing and Reporting Biases 158 Reporting Power 160 Drawing Conclusions 160 Publishing Statistical Theory 162 A Slippery Slope 162 Summary 163 To Learn More 163 9. Interpreting Reports 165 With a Grain of Salt 165 The Authors 166 Cost--Benefit Analysis 167 The Samples 167 Aggregating Data 168 Experimental Design 168 Descriptive Statistics 169 The Analysis 169 Correlation and Regression 171 Graphics 171 Conclusions 172 Rates and Percentages 174 Interpreting Computer Printouts 175 Summary 178 To Learn More 178 10. Graphics 181 Is a Graph Really Necessary? 182 KISS 182 The Soccer Data 182 Five Rules for Avoiding Bad Graphics 183 One Rule for Correct Usage of Three-Dimensional Graphics 194 The Misunderstood and Maligned Pie Chart 196 Two Rules for Effective Display of Subgroup Information 198 Two Rules for Text Elements in Graphics 201 Multidimensional Displays 203 Choosing Effective Display Elements 209 Oral Presentations 209 Summary 210 To Learn More 211 PART III BUILDING A MODEL 213 11. Univariate Regression 215 Model Selection 215 Stratification 222 Further Considerations 226 Summary 233 To Learn More 234 12. Alternate Methods of Regression 237 Linear Versus Nonlinear Regression 238 Least-Absolute-Deviation Regression 238 Quantile Regression 243 Survival Analysis 245 The Ecological Fallacy 246 Nonsense Regression 248 Reporting the Results 248 Summary 248 To Learn More 249 13. Multivariable Regression 251 Caveats 251 Dynamic Models 256 Factor Analysis 256 Reporting Your Results 258 A Conjecture 260 Decision Trees 261 Building a Successful Model 264 To Learn More 265 14. Modeling Counts and Correlated Data 267 Counts 268 Binomial Outcomes 268 Common Sources of Error 269 Panel Data 270 Fixed- and Random-Effects Models 270 Population-Averaged Generalized Estimating Equation Models (GEEs) 271 Subject-Specific or Population-Averaged? 272 Variance Estimation 272 Quick Reference for Popular Panel Estimators 273 To Learn More 275 15. Validation 277 Objectives 277 Methods of Validation 278 Measures of Predictive Success 283 To Learn More 285 Glossary 287 Bibliography 291 Author Index 319 Subject Index 329