Common Errors in Statistics (and How to Avoid Them) - Tapa blanda

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PHILLIP I. GOOD, PhD, is Operations Manager of Information Research, a consulting firm specializing in statistical solutions for private and public organizations and has published eighteen books. JAMES W. HARDIN, PhD, is Associate Research Professor in the Department of Epidemiology and Biostatistics at the University of South Carolina.

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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.