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

by Phillip I. Good (statcourse.com ); James W. Hardin (Univ. of South Carolina )

• ISBN13: 9780470457986
• ISBN10: 0470457988
• Edition: 3rd
• Format: Paperback
• Copyright: 2009-07-01
• Publisher: Wiley

Summary

The previous editions have proven to be successful guides for choosing and using the right techniques. Common Errors is consistently coherent and provides a consistent level throughout. The Third Edition elaborates on many key topics such as response variables, errors in testing hypothesis, higher order experimental design, curve fitting and magic beans, Poisson and negative binomial regression, correcting for confounding variables, dynamic models, factor analysis, general linear models, decision trees, etc. One new chapter has been added on "Statistical Analysis" and includes sections on data quality assessment, data review, and design review. Modifications have been included throughout the book, and many new figures have also been added. Topics covered include creating a research plan, collecting data, formulating and testing a hypothesis, estimating coefficients, specifying sample size, checking assumptions, interpreting p-values and confidence intervals, building a model, reporting results, data mining, Bayes' Theorem, the bootstrap, and many others.

Author Biography

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.

Table of Contents

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