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Common Errors in Statistics (And How to Avoid Them)

Phillip I. Good and James W. Hardin
Publisher: 
John Wiley
Publication Date: 
2006
Number of Pages: 
254
Format: 
Paperback
Edition: 
2
Price: 
44.95
ISBN: 
0471794317
Category: 
Monograph
[Reviewed by
Sarah Boslaugh
, on
06/22/2006
]

Statistical software packages seem to get more user-friendly each year, but as anyone who interacts with novice users of statistics knows, that's not necessarily a good thing. "Back in the day," I think in my more curmudgeonly moments, "we had to actually understand what we were doing in order to get an analysis to run". Of course that's not entirely true: it's always been possible to misuse statistics, unknowingly or otherwise, it's just gotten easier and quicker to do it from your laptop.

If Common Errors in Statistics (and How to Avoid Them) has an over-riding theme, it's this: don't let the software do your thinking for you. Good and Hardin's popular guide to the major issues of statistics emphasizes logical thinking in the application and interpretation of statistics, and largely leaves the formulas to other texts. In fact, most of Common Errors in Statistics can be understood by the statistically-uninitiated, who may need to skip over the few sections which are thick with Greek notation. Most of the text provides a concise guide to the basics of statistics, replete with examples, explained in common language with an emphasis on meaning and understanding rather than calculation. It's a valuable reference volume for more advanced statisticians as well: it's worth having simply to remind oneself of the most common statistical pitfalls before undertaking a new project, and for the  references included in each chapter.

Common Errors in Statistics covers all the expected topics: hypothesis testing, choosing an appropriate statistic, reporting results, using graphics, etc. It also includes brief discussions of more advanced techniques, such as nonlinear regression and classification and regression trees. In addition, it includes the most useful glossary I have ever seen in a statistics book, which is arranged by what Good and Hardin term "related but distinct terms". If you have a habit of confusing, say, accuracy and precision, this glossary will set you straight.

Good and Hardin teach online courses at statistics.com and are experienced consultants and teachers. Good is also Operations Manager for the statistical consulting firm Information Research and Hardin is an Associate Research Professor in the Department of Epidemiology and Biostatistics at the University of South Carolina. This practical experience is evident through Common Errors in Statistics as they demonstrate their ability to explain statistical concepts to novices and to predict exactly where an analysis is likely to run off the rails. It would not serve as a textbook in statistics, due to the lack of formulas and exercises, but would be a valuable second text in an introductory course.


Sarah Boslaugh,  (boslaugh_s@kids.wustl.edu) is a Senior Statistical Data Analyst in the Department of Pediatrics at the Washington University School of Medicine in St. Louis, MO. She wrote An Intermediate Guide to SPSS Programming: Using Syntax for Data Management for Sage Publications in 2005 and is currently writing Secondary Data Sources for Public Health: A Practical Guide for Cambridge University Press. She is also Editor-in-Chief of The Encyclopedia of Epidemiology which will be published by Sage in 2007.

 

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 Statistical Procedures.

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.

Dummy View - NOT TO BE DELETED