Common Errors in Statistics (and How to Avoid Them)
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 . . . "
" . . . 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."
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.
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ˆ ˆ ˆr ˆr ˆr 2006 John Wiley alternative hypothesis Altman analysis apparent error apply associated assumptions Bayes Bayes factor bias bootstrap bootstrap sample Chapter coefficients Common Errors confidence interval correct correlation cross-validation data set Deming regression denote depend difference equation Errors in Statistics example expected excess error experiment experimental design F-ratio factor Figure goals scored graphic Gregory’s Hardin independent interval estimate jackknife labels large number linear regression logistic regression loss function main effects mean measure median meta-analysis methods Node normal distribution null hypothesis number of goals observations obtain outcome p-value parameter patients permutation test plot population prediction rule predictor prior probability problem random sample sizes significance level simulations specific square statistical procedures statistically significant subgroup t-test tables Team test statistic tion Total Number treatment true Type II error validation values variance zero
Side 232 - ISIS-2 (Second International Study of Infarct Survival) Collaborative Group. Randomized trial of intravenous streptokinase, oral aspirin, both, or neither among 17,187 cases of suspected acute myocardial infarction.