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.

**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®, Introduction to Statistics Through Resampling Methods and Microsoft Office Excel®, *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**