Common Errors in Statistics (and How to Avoid Them)

Praise for the Second Edition "All statistics students and teachers will find in this book a friendly and intelligentguide to . . . applied statistics in practice." —Journal of Applied Statistics ". . . a very engaging and valuable book for all who use statistics in any setting." —CHOICE ". . . a concise guide to the basics of statistics, replete with examples . . . a valuablereference for more advanced statisticians as well." —MAA Reviews Now in its Third Edition, the highly readable Common Errors in Statistics (and How to Avoid Them) continues to serve as a thorough and straightforward discussion of basic statistical methods, presentations, approaches, and modeling techniques. Further enriched with new examples and counterexamples from the latest research as well as added coverage of relevant topics, this new edition of the benchmark book 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. The Third Edition has been considerably expanded and revised to include: A new chapter on data quality assessment A new chapter on correlated data An expanded chapter on data analysis covering categorical and ordinal data, continuous measurements, and time-to-event data, including sections on factorial and crossover designs Revamped exercises with a stronger emphasis on solutions An extended chapter on report preparation New sections on factor analysis as well as Poisson and negative binomial regression Providing valuable, up-to-date information in the same user-friendly format as its predecessor, Common Errors in Statistics (and How to Avoid Them), Third Edition is an excellent book for students and professionals in industry, government, medicine, and the social sciences.

Table of Contents:
PREFACE xi PART I FOUNDATIONS 1 1 Sources of Error 3 Prescription, 4 Fundamental Concepts, 5 Ad Hoc, Post Hoc Hypotheses, 7 To Learn More, 11 2 Hypotheses: The Why of Your Research 13 Prescription, 13 What is a Hypothesis?, 14 Found Data, 16 Null Hypothesis, 16 Neyman–Pearson Theory, 17 Deduction and Induction, 21 Losses, 22 Decisions, 23 To Learn More, 25 3 Collecting Data 27 Preparation, 27 Response Variables, 28 Determining Sample Size, 32 Sequential Sampling, 36 One-Tail or Two?, 37 Fundamental Assumptions, 40 Experimental Design, 41 Four Guidelines, 43 Are Experiments Really Necessary?, 46 To Learn More, 47 PART II STATISTICAL ANALYSIS 49 4 Data Quality Assessment 51 Objectives, 52 Review the Sampling Design, 52 Data Review, 53 The Four-Plot, 55 To Learn More, 55 5 Estimation 57 Prevention, 57 Desirable and Not-So-Desirable Estimators, 57 Interval Estimates, 61 Improved Results, 65 Summary, 66 To Learn More, 66 6 Testing Hypotheses: Choosing a Test Statistic 67 First Steps, 68 Test Assumptions, 70 Binomial Trials, 71 Categorical Data, 72 Time-to-Event Data (Survival Analysis), 73 Comparing the Means of Two Sets of Measurements, 76 Comparing Variances, 85 Comparing the Means of k Samples, 89 Subjective Data, 91 Independence Versus Correlation, 91 Higher-Order Experimental Designs, 92 Inferior Tests, 96 Multiple Tests, 97 Before You Draw Conclusions, 97 Summary, 99 To Learn More, 99 7 Miscellaneous Statistical Procedures 101 Bootstrap, 102 Bayesian Methodology, 103 Meta-Analysis, 110 Permutation Tests, 112 To Learn More, 113 PART III REPORTS 115 8 Reporting Your Results 117 Fundamentals, 117 Descriptive Statistics, 122 Standard Error, 127 p-Values, 130 Confidence Intervals, 131 Recognizing and Reporting Biases, 133 Reporting Power, 135 Drawing Conclusions, 135 Summary, 136 To Learn More, 136 9 Interpreting Reports 139 With a Grain of Salt, 139 The Analysis, 141 Rates and Percentages, 145 Interpreting Computer Printouts, 146 To Learn More, 146 10 Graphics 149 The Soccer Data, 150 Five Rules for Avoiding Bad Graphics, 150 One Rule for Correct Usage of Three-Dimensional Graphics, 159 The Misunderstood and Maligned Pie Chart, 161 Two Rules for Effective Display of Subgroup Information, 162 Two Rules for Text Elements in Graphics, 166 Multidimensional Displays, 167 Choosing Graphical Displays, 170 Summary, 172 To Learn More, 172 PART IV BUILDING A MODEL 175 11 Univariate Regression 177 Model Selection, 178 Stratification, 183 Estimating Coefficients, 185 Further Considerations, 187 Summary, 191 To Learn More, 192 12 Alternate Methods of Regression 193 Linear Versus Non-Linear Regression, 194 Least Absolute Deviation Regression, 194 Errors-in-Variables Regression, 196 Quantile Regression, 199 The Ecological Fallacy, 201 Nonsense Regression, 202 Summary, 202 To Learn More, 203 13 Multivariable Regression 205 Caveats, 205 Correcting for Confounding Variables, 207 Keep It Simple, 207 Dynamic Models, 208 Factor Analysis, 208 Reporting Your Results, 209 A Conjecture, 211 Decision Trees, 211 Building a Successful Model, 214 To Learn More, 215 14 Modeling Correlated Data 217 Common Sources of Error, 218 Panel Data, 218 Fixed- and Random-Effects Models, 219 Population-Averaged GEEs, 219 Quick Reference for Popular Panel Estimators, 221 To Learn More, 223 15 Validation 225 Objectives, 225 Methods of Validation, 226 Measures of Predictive Success, 229 Long-Term Stability, 231 To Learn More, 231 GLOSSARY, GROUPED BY RELATED BUT DISTINCT TERMS 233 BIBLIOGRAPHY 237 AUTHOR INDEX 259 SUBJECT INDEX 267

About the Author :
PHILLIP I. GOOD, PhD, is Operations Manager of Statcourse.com, a consulting firm specializing in statistical solutions for industry. He has published more than thirty scholarly works, more than six hundred popular articles, and twenty-one books, including Introduction to Statistics Through Resampling Methods and R/S-PLUS and Introduction to Statistics Through Resampling Methods and Microsoft Office Excel, both published by Wiley. JAMES W. HARDIN, PhD, is Research Associate Professor and Director of the Biostatistics Collaborative Unit at the University of South Carolina.

Review :
"The new edition incorporates more graphics and examples using more recent data. … Good's advice is usually wise, and always worth considering. Recommended as stimulating reading for the statistical sophisticate." (Journal of Biopharmaceutical Statistics, January 2010)