Common Errors in Statistics (and How to Avoid Them)
2nd Edition
Published on 25. July 2006
254 pages
978-0-471-99851-8 (ISBN)
Description
Praise for the First Edition of Common Errors in Statistics
" . . . let me recommend Common Errors to all those who interactwith statistics, whatever their level of statistical understanding. . . "
--Stats 40
" . . . written . . . for the people who define good practicerather than seek to emulate it."
--Journal of Biopharmaceutical Statistics
" . . . highly informative, enjoyable to read, and of potential useto a broad audience. It is a book that should be on the referenceshelf of many statisticians and researchers."
--The American Statistician
" . . . I found this book the most easily readable statistics bookever. The credit for this certainly goes to Phillip Good."
--E-STREAMS
A tried-and-true guide to the proper application ofstatistics
Now in a second edition, the highly readable Common Errors inStatistics (and How to Avoid Them) lays a mathematically rigorousand readily accessible foundation for understanding statisticalprocedures, problems, and solutions. This handy field guideanalyzes common mistakes, debunks popular myths, and helps readersto choose the best and most effective statistical technique foreach of their tasks.
Written for both the newly minted academic and the professional whouses statistics in their work, the book covers creating a researchplan, formulating a hypothesis, specifying sample size, checkingassumptions, interpreting p-values and confidence intervals,building a model, data mining, Bayes' Theorem, the bootstrap, andmany other topics. The Second Edition has been extensively revisedto include:
* Additional charts and graphs
* Two new chapters, Interpreting Reports and Which RegressionMethod?
* New sections on practical versus statistical significance andnonuniqueness in multivariate regression
* Added material from the authors' online courses atstatistics.com
* New material on unbalanced designs, report interpretation, andalternative modeling methods
With a final emphasis on both finding solutions and the great valueof statistics when applied in the proper context, this book iseminently useful to students and professionals in the fields ofresearch, industry, medicine, and government.
Reviews / Votes
"In summary, I think the book does achieve its mission tohighlight common errors in statistical analysis." (Significance, 1 December 2006) "I regard this interesting book to be of potential use to abroad audience." (Statistical Papers, August 2007) "A very engaging and valuable book for all who use statistics inany settings." (CHOICE, October 2006) "...a concise guide to the basics of statistics, replete withexamples, explaining in common language...a valuable reference formore advanced statisticians as well..." (MAA Reviews, June22, 2006) "All statistics students and teachers will find in thisbook a friendly and intelligent guide to...applied statisticsin practice." (Journal Of Applied Statistics, April2007)More details
Other editions
New editions
Phillip I. Good | James W. Hardin
Common Errors in Statistics (and How to Avoid Them)
E-Book
09/2011
3rd Edition
Wiley
€45.99
Available for download
Additional editions
Phillip I. Good | James W. Hardin
Common Errors in Statistics (and How to Avoid Them)
Software
04/2006
2nd Edition
Wiley
€35.73
Article exhausted; check different version
Phillip I. Good | James W. Hardin
Common Errors in Statistics (and How to Avoid Them)
Book
04/2006
2nd Edition
Wiley
€72.90
Article exhausted; check for reprint
Persons
PHILLIP I. GOOD, PhD, is Operations Manager of InformationResearch, a consulting firm specializing in statistical solutionsfor private and public organizations and has published eighteenbooks.
JAMES W. HARDIN, PhD, is Associate Research Professor inthe Department of Epidemiology and Biostatistics at the Universityof South Carolina.
Content
Preface.
PART I FOUNDATIONS.
1. Sources of Error.
Prescription.
Fundamental Concepts.
Ad Hoc, Post Hoc Hypotheses.
2. Hypotheses: The Why of Your Research.
Prescription.
What Is a Hypothesis?
How precise must a hypothesis be?
Found Data.
Null hypothesis.
Neyman-Pearson Theory.
Deduction and Induction.
Losses.
Decisions.
To Learn More.
3. Collecting Data.
Preparation.
Measuring Devices.
Determining Sample Size.
Fundamental Assumptions.
Experimental Design.
Four Guidelines.
Are Experiments Really Necessary?
To Learn More.
PART II HYPOTHESIS TESTING AND ESTIMATION.
4. Estimation.
Prevention.
Desirable and Not-So-Desirable Estimators.
Interval Estimates.
Improved Results.
Summary.
To Learn More.
5. Testing Hypotheses: Choosing a Test Statistic.
Comparing Means of Two Populations.
Comparing Variances.
Comparing the Means of K Samples.
Higher-Order Experimental Designs.
Contingency Tables.
Inferior Tests.
Multiple Tests.
Before You Draw Conclusions.
Summary.
To Learn More.
6. Strengths and Limitations of Some Miscellaneous StatisticalProcedures.
Bootstrap.
Bayesian Methodology.
Meta-Analysis.
Permutation Tests.
To Learn More.
7. Reporting Your Results.
Fundamentals.
Tables.
Standard Error.
p-Values.
Confidence Intervals.
Recognizing and Reporting Biases.
Reporting Power.
Drawing Conclusions.
Summary.
To Learn More.
8. Interpreting Reports.
With A Grain of Salt.
Rates and Percentages.
Interpreting Computer Printouts.
9. Graphics.
The Soccer Data.
Five Rules for Avoiding Bad Graphics.
One Rule for Correct Usage of Three-Dimensional Graphics.
The Misunderstood Pie Chart.
Two Rules for Effective Display of Subgroup Information.
Two Rules for Text Elements in Graphics.
Multidimensional Displays.
Choosing Graphical Displays.
Summary.
To Learn More.
PART III BUILDING A MODEL.
10. Univariate Regression.
Model Selection.
Estimating Coefficients.
Further Considerations.
Summary.
To Learn More.
11. Alternate Methods of Regression.
Linear vs. Nonlinear Regression.
Least Absolute Deviation Regression.
Errors-in-Variables Regression.
Quantile Regression.
The Ecological Fallacy.
Nonsense Regression.
Summary.
To Learn More.
12. Multivariable Regression.
Caveats.
Factor Analysis.
General Linearized Models.
Reporting Your Results.
A Conjecture.
Decision Trees.
Building a Successful Model.
To Learn More.
13. Validation.
Methods of Validation.
Measures of Predictive Success.
Long-Term Stability.
To Learn More.
Appendix A.
Appendix B.
Glossary, Grouped by Related but Distinct Terms.
Bibliography.
Author Index.
Subject Index.
