Synopses & Reviews
Praise for the First Edition of Common Errors in Statistics" . . . let me recommend Common Errors to all those who interact with statistics, whatever their level of statistical understanding . . . "
—Stats 40
" . . . written . . . for the people who define good practice rather than seek to emulate it."
—Journal of Biopharmaceutical Statistics
" . . . highly informative, enjoyable to read, and of potential use to a broad audience. It is a book that should be on the reference shelf of many statisticians and researchers."
—The American Statistician
" . . . I found this book the most easily readable statistics book ever. The credit for this certainly goes to Phillip Good."
—E-STREAMS
A tried-and-true guide to the proper application of statistics
Now in a second edition, the highly readable Common Errors in Statistics (and How to Avoid Them)lays a mathematically rigorous and readily accessible foundation for understanding statistical procedures, problems, and solutions. This handy field guide analyzes common mistakes, debunks popular myths, and helps readers to choose the best and most effective statistical technique for each of their tasks.
Written for both the newly minted academic and the professional who uses statistics in their work, the book covers creating a research plan, formulating a hypothesis, specifying sample size, checking assumptions, interpreting p-values and confidence intervals, building a model, data mining, Bayes' Theorem, the bootstrap, and many other topics. The Second Edition has been extensively revised to include:
- Additional charts and graphs
- Two new chapters, Interpreting Reports and Which Regression Method?
- New sections on practical versus statistical significance and nonuniqueness in multivariate regression
- Added material from the authors' online courses at statistics.com
- New material on unbalanced designs, report interpretation, and alternative modeling methods
With a final emphasis on both finding solutions and the great value of statistics when applied in the proper context, this book is eminently useful to students and professionals in the fields of research, industry, medicine, and government.
Review
"Out of a tangle of complexity (which results from telling it like it is) comes a great deal of very good advice." (
Journal of Quality Technology, January 2005)
"...written...for the people who define good practice rather than seek to emulate it." (Journal of Biopharmaceutical Statistics, 2004)
"...highly informative, enjoyable to read, and of potential use to a broad audience. It is a book that should be on the reference shelf of many statisticians and researchers." (The American Statistician, November 2004)
"…I found this book the most easily readable statistics book ever. The credit for this certainly goes to Phillip Good.” (E-STREAMS, September 2004)
“…useful to students and professionals in the fields of research, industry, medicine, and government.” (Zentralblatt Math, Vol. 1032, No.7, 2004)
“…provides an easily understood foundation for statistical practice...clearly written and well divided into short sections” (CMRO- Current Medical Research & Opinions, Vol.20 No. 7, 2004)
"So, let me recommend 'CE' to all those who interact with statistics, whatever their level of statistical understanding…” (Stats 40, Spring 2004)
"An excellent resource. Highly recommended.” (Choice, June 2004, Vol. 41 No. 10)
Synopsis
A guide to choosing and using the right techniques
High-speed computers and prepackaged statistical routines would seem to take much of the guesswork out of statistical analysis and lend its applications readily accessible to all. Yet, as Phillip Good and James Hardin persuasively argue, statistical software no more makes one a statistician than a scalpel makes one a surgeon. Choosing the proper technique and understanding the analytical context is of paramount importance to the proper application of statistics. The highly readable Common Errors in Statistics (and How to Avoid Them) provides both newly minted academics and professionals who use statistics in their work with a handy field guide to statistical problems and solutions.
Good and Hardin begin their handbook by establishing a mathematically rigorous but readily accessible foundation for statistical procedures. They focus on debunking popular myths, analyzing common mistakes, and instructing readers on how to choose the appropriate statistical technique to address their specific task. A handy checklist is provided to summarize the necessary steps.
Topics covered include:
- Creating a research plan
- Formulating a hypothesis
- Specifying sample size
- Checking assumptions
- Interpreting p-values and confidence intervals
- Building a model
- Data mining
- Bayes Theorem, the bootstrap, and many others
Common Errors in Statistics (and How to Avoid Them) also contains reprints of classic articles from statistical literature to re-examine such bedrock subjects as linear regression, the analysis of variance, maximum likelihood, meta-analysis, and the bootstrap. With a final emphasis on finding solutions and on the great value of statistics when applied in the proper context, this book will prove eminently useful to students and professionals in the fields of research, industry, medicine, and government.
Synopsis
Includes bibliographical references (p. 191-209) and index.
About the Author
PHILLIP I. GOOD, PhD, is an Operations Manager for Information Research in Huntington Beach, California. He is the author of A Manager’s Guide to the Design and Conduct of Clinical Trials, published by Wiley, as well as numerous other books.
JAMES W. HARDIN, PhD, is a lecturer and Assistant Research Scientist in the Department of Statistics at Texas A&M University.
Table of Contents
Preface.
Part I. Foundations.
1. Sources of Error.
2. Hypotheses: The Why of Your Research.
3. Collecting Data.
Part II. Hypothesis Testing and Estimation.
4. Estimation.
5. Testing Hypotheses: Choosing a Test Statistic.
6. Strengths and Limitations of Some Miscellaneous Statistical Procedures.
7. Reporting Your Results.
8. Graphics.
Part III. Building a Model.
9. Univariate Regression.
10. Multivariate Regression.
11. Validation.
Appendix A.
Appendix B.
Glossary, Grouped by Related but Distinct Terms.
Bibliography.
Author Index.
Subject Index.