Common Errors in Statistics:

“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)

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.