Common errors in statistics (and how to avoid them)

Phillip I. Good, James W. Hardin

232 pages, parution le 14/10/2003

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Résumé

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.

Foundations
- Sources of Error
- Hypotheses: The Why of Your Research
- Collecting Data
Hypothesis Testing and Estimation
- Estimation
- Testing Hypotheses: Choosing a Test Statistic
- Strengths and Limitations of Some Miscellaneous Statistical Procedures
- Reporting Your Results
- Graphics
Building a Model
- Univariate Regression
- Multivariate Regression
- Validation
Appendices
Glossary, Grouped by Related but Distinct Terms
Bibliography
Author Index
Subject Index

L'auteur - Phillip I. Good

PHILLIP I. GOOD, PhD, is an Operations Manager for Information Research in Huntington Beach, California. He is the author of A Manager's Guide to the Design and Conduct of Clinical Trials, published by Wiley, as well as numerous other books.

L'auteur - James W. Hardin

JAMES W. HARDIN, PhD, is a lecturer and Assistant Research Scientist in the Department of Statistics at Texas A&M University.

Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	Phillip I. Good, James W. Hardin
Parution	14/10/2003
Nb. de pages	232
Format	15,5 x 23,5
Couverture	Broché
Poids	415g
Intérieur	Noir et Blanc
EAN13	9780471460688