Common Errors in Statistics (and How to Avoid Them) / Edition 3

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Overview

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.

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Editorial Reviews

From the Publisher
“Presented in an easy-to-follow style, this textbook is thought for students and professionals in industry, government, medicine, and the social sciences.” (Zentralblatt MATH, 1 December 2013)
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Product Details

  • ISBN-13: 9780470457986
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 7/7/2009
  • Edition number: 3
  • Pages: 288
  • Product dimensions: 6.10 (w) x 9.10 (h) x 0.70 (d)

Meet the Author

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.

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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

Fundamentals117

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

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Sort by: Showing all of 3 Customer Reviews
  • Posted October 11, 2009

    more from this reviewer

    How to Make a More Cogent Use of Statistics

    Phillip Good and James Hardin often succeed in their endeavor to make their content accessible to an audience beyond that of "hardcore" statisticians. Despite their many applications in any modern society, statistics look unappealing to most people. Sometimes, both authors get lost in esoteric debates about some statistical topics that are of limited interest to a wider audience. Furthermore, Good and Hardin give too many examples that are related to the medical field. The authors could diversify their examples in a fourth edition of their treatise to further expand their readership. To their credit, Good and Hardin repeatedly warn their audience against the servile reliance on statistical software. Software users have to check the default settings to see if they are applicable to the application at hand. The authors correctly note that the most common error in statistics is to assume that statistical procedures can take the place of sustained effort. For this reason, Good and Hardin urge their readers not to let statistics and by extension statistical software do their thinking for them. In conclusion, "Common Errors in Statistics and How to Avoid Them" is a nice addition to anyone's modeling / statistical library.

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    Posted December 7, 2009

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