The A-Z of Error-Free Research

"Preface This new text is designed to assist you in the transition from slavish student to bold independent researcher. Use it for a self-help refresher course, as a textbook for a course in research methods, and as a second course in statistics. It contains step-by-step prescriptions, numerous worked through examples, and the R code you'll need to implement the methods aid in your making use of the material. You will learn when to use statistics, the best ways to cope with variation, how to design an experiment, how to determine optimal sample size, and how to collect useable data for experiments, clinical trials, and surveys. You will be guided to the best statistical procedures for your current application and taken step by step through model development and reporting your results for publication. Chapter 1 provides an overall prescription along with guidance as to when and when not to use statistics. Chapters 2, 3, and 4 walk us through the planning phase. Chapters 5 and 6 take us step by step through data collection. Methods for sample size determination are deferred to Chapter 11. A comprehensive guide to contemporary methods in data analysis is provided in Chapter 7 through Chapter 10. Techniques for developing models that will provide a basis forfuture research are given in Chapters 12, 13, and 14. Chapters 15 through 17 describe reporting techniques that will ensure your research efforts get the credit they deserve. Chapter 18 is devoted to case-control and cohort studies. Put this book to practical use today. Your comments are welcomed"--Provided by publisher.

Phillip I. Good is a mathematical statistician with nearly 40 years of experience in the field. He has published hundreds of articles on microcomputers and has authored nine books, including A Practitioner’s Guide to Resampling for Data Analysis, Data Mining, and Modeling and Applying Statistics in the Courtroom: A New Approach for Attorneys and Expert Witnesses. He was among the first to apply the bootstrap method in his analyses of 2 × 2 designs with a missing cell. He has also contributed to other areas of small sample statistics, including a uniformly most powerful unbiased permutation test for Type I censored data, an exact test for comparing variances, and an exact test for cross-over designs. He earned a PhD in mathematical statistics from the University of California at Berkeley.