Common Errors in Statistics (And How to Avoid Them), Fourth Edition

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