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Praise for Common Errors in Statistics (and How to Avoid Them)
"A very engaging and valuable book for all who use statistics in any setting."
—CHOICE
"Addresses popular mistakes often made in data collection and provides an indispensable guide to accurate statistical analysis and reporting. The authors' emphasis on careful practice, combined with a focus on the development of solutions, reveals the true value of statistics when applied correctly in any area of research."
—MAA Reviews
Common Errors in Statistics (and How to Avoid Them), Fourth Edition provides a mathematically rigorous, yet readily accessible foundation in statistics for experienced readers as well as students learning to design and complete experiments, surveys, and clinical trials.
Providing a consistent level of coherency throughout, the highly readable Fourth Edition focuses on debunking popular myths, analyzing common mistakes, and instructing readers on how to choose the appropriate statistical technique to address their specific task. The authors begin with an introduction to the main sources of error and provide techniques for avoiding them. Subsequent chapters outline key methods and practices for accurate analysis, reporting, and model building. The Fourth Edition features newly added topics, including:
- Baseline data
- Detecting fraud
- Linear regression versus linear behavior
- Case control studies
- Minimum reporting requirements
- Non-random samples
The book concludes with a glossary that outlines key terms, and an extensive bibliography with several hundred citations directing readers to resources for further study.
Presented in an easy-to-follow style, Common Errors in Statistics, Fourth Edition is an excellent book for students and professionals in industry, government, medicine, and the social sciences.
- Sales Rank: #214385 in eBooks
- Published on: 2012-06-07
- Released on: 2012-06-07
- Format: Kindle eBook
Review
“Presented in an easy-to-follow style, this textbook is thought for students and professionals in industry, government, medicine, and the social sciences.” (Zentralblatt MATH, 1 December 2013)
About the Author
PHILLIP I. GOOD, PhD, is Operations Manager at Information Research, a consulting firm specializing in statistical solutions for private and public organizations. He has published more than thirty scholarly works and more than 600 popular articles. Dr. Good is the author of Introduction to Statistics Through Resampling Methods and R/S-PLUS®, Introduction to Statistics Through Resampling Methods and Microsoft Office Excel®, and Analyzing the Large Number of Variables in Biomedical and Satellite Imagery, all published by Wiley.
JAMES W. HARDIN, PhD, is Associate Professor and Biostatistics Division Director of the Department of Epidemiology and Biostatistics at the University of South Carolina. Dr. Hardin has published extensively in his areas of research interest, which include generalized linear models, generalized estimating equations, survival models, and computational statistics. He is also an affiliate faculty member of the Institute for Families in Society at the University of South Carolina.
Most helpful customer reviews
13 of 14 people found the following review helpful.
Important and useful
By Dr. Peter Davies
This is a good book. Its purpose is a noble one- it's ultimately about helping us tell truth from fiction. It's about spotting lies made through statistics through leaving things out, distorting findings, torturing data, and generally misbehaving with numbers, This misbehaviour may be innocent but ignorant, or deliberate and deceitful.
This book is a thorough description of how to use statistics well, which measurements and tests to use when, and where. It is mostly understandable, but I did not understand the detailed maths within it. I think this is my loss- it's probably something I should know, and probably something all those who plan and lead experiments should understand. The examples illustrating the main points were well chosen. It is well written with a nice vein of humour running through it. It is written at a high level- I think aimed at university science and maths students and above. It achieves it purpose of teaching us how to get the numbers right, and its second purpose of showing us how to avoid falling into error. The fact that it is so easy to find basic statistical mistakes in published papers should worry us- the errors are sufficient to invalidate these papers and their conclusions. The fact that peer review picks up so few of these errors suggests that statisticians are not part of the reviewing team often enough. It also reduces the credibility of the peer review process showing that it is a process that fails to pick up and prevent errors in published work.
After reading Ben Goldacre's book, Bad Pharma: How drug companies mislead doctors and harm patients the need for this book is all too obvious. Other books on this theme would include the easier How to Lie with Statistics (Penguin Mathematics) and IrrationalityI have a feeling that in my training as a doctor certain topics were left out of the course that should have been taught formally and fully. The design of research should have been covered more- both to do it and to understand its results better, and Patient Safety should have been a formal course too.
This book speaks to a gap in many people's understanding and use of statistics. If I could understand it fully what Hemingway described as "my internal crap detector" would be strengthened significantly. As it was even though I think understood about half of it, I still feel I got a lot out of it.
If you are a science student, or working as a researcher with numerical data then this book will be very useful to you. You will design your experiments better, ask the right questions of your data, and present your results more fairly, and more clearly.
5 of 6 people found the following review helpful.
Great advice on what not to do
By S. Bierner
It's not often I can say a statistics book is enjoyable to read. This volume is quite a joy: well written and full of useful examples so amateur users of statistics (scientists and clinical researchers) cam avoid mistakes that would otherwise ruin your data analysis. References are up to date and very useful as well.
3 of 4 people found the following review helpful.
Some gems buried here
By Amazon Customer
There are some excellent suggestions for the applied statistician/data analyst in this book. These nuggets, however, are buried between many topics I didn't find particularly helpful. I tend to pick this book up, read a few pages, skip a few pages, and then put it down until I notice it on my bookshelf again.
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