Common Errors in Statistics 2nd Edition

“All statistics students and teachers will find in this book a
friendly and intelligent guide to…applied statistics in practice.” — Journal Of Applied Statistics, April 2007

“I regard this interesting book to be of potential use to a broad audience.”
— Statistical Papers, August 2007

Praise for the First Edition of Common Errors in Statistics 2nd Edition

” . . . 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.