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

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Authors:
Phillip I. Good and James W. Hardin
Publisher: Wiley
Copyright: 2012
ISBN-13: 978-1-118-29439-0
Pages: 336; paperback
Price: $42.50

Comment from the Stata technical group

Common Errors in Statistics (and How to Avoid Them), Fourth Edition, by Phillip I. Good and James W. Hardin, contains a wealth of advice on how to improve experimental design, produce informative tables and graphs, and effectively analyze data. This book is not a treatise on statistical theory. Rather, it provides information on how to best apply that theory to real-world applications and obtain informative results. As the title implies, the book provides many examples of poorly executed analyses and then explains in detail how those examples can be improved.

The authors begin by discussing foundational issues of statistical analysis, including sources of error, data collection, and hypothesis formation. Chapter 2, on hypotheses, has been completely rewritten and now emphasizes the importance of formulating a null hypothesis and all the alternatives, including the conclusions that you would draw based on the outcome you later obtain. The chapter also discusses traditional Neyman–Pearson testing as well as decision making.

The second part of the book focuses on hypothesis testing and parameter estimation. Here the authors examine the statistical evaluation of the data as well as the strengths and limitations of various statistical procedures. Chapter 8, on how to report results, has been updated to reflect the strengths and weaknesses of p-values and confidence intervals as well as to show the important distinction between the statistical significance and the practical significance of results. Chapter 10 discusses how to make effective graphs and includes a list of 11 helpful rules to follow.

The last part of the book shows how to build a model, including linear and nonlinear regression, quantile regression, count models, and panel (longitudinal) data models. The final chapter discusses model validation.

The applied exposition in Common Errors in Statistics (and How to Avoid Them), Fourth Edition will be useful to experienced practitioners, and its many examples and careful explanations make it a helpful supplemental textbook for students of statistics.

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