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Full Description
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.
Contents
Preface. Part I. Foundations. 1. Sources of Error. 2. Hypotheses: The Why of Your Research. 3. Collecting Data. Part II. Hypothesis Testing and Estimation. 4. Estimation. 5. Testing Hypotheses: Choosing a Test Statistic. 6. Strengths and Limitations of Some Miscellaneous Statistical Procedures. 7. Reporting Your Results. 8. Graphics. Part III. Building a Model. 9. Univariate Regression. 10. Multivariate Regression. 11. Validation. Appendix A. Appendix B. Glossary, Grouped by Related but Distinct Terms. Bibliography. Author Index. Subject Index.
