Customer Reviews

Statistical Rules of Thumb (Wiley Series in Probability and Statistics)

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What a great idea. Rather than write a comprehensive text, Dr. VanBelle writes about a large number of statistical topics, focussing on areas that are confusing, or frequently misunderstood. Some of his "rules of thumbs" are approximate formulae for doing quick (but approximate) analyses. Most are more general advice, based on decades of consulting. Topics range from designing studies to making graphs, but most are about data analysis. Something for everyone in a well-written book.

Excellent reference for statisticians. Only two complaints:
1) In many instances, wording is not clear - you have to really pick sentences apart to figure out what the author meant.
2) Reasons for a particular rule sometimes leave you wanting. But at least you're introduced to the concept and can look elsewhere for assistance in understanding.

Also, the title might lead some non-statisticans to think that they can pick this book up and learn how to plug and chug in all sorts of situations. This is not the case.

Gerald van Belle is a biostatistician and professor at the University of Washington. He has coauthored an excellent text on biostatistics with Lloyd Fisher. In this delightful and clearly written text van Belle provides 99 rules of thumb based on his vast experience as a consultant and researcher in statistics and biostatistics. For a statistician or a student of statistics along with the term "rule of thumb" one thinks of the three sigma rule or the use of range divided by sample size as a quick estimate of standard error of the mean. But this text is much more than a compilation of such simple rules. Professor van Belle organizes the book into topical chapters on sample size determination, covariation, epidemiology, environmental studies, design, conduct and analysis of experiments, tables and graphics, and consulting. Each rule is put into proper context and is justified with mathemetical theorems or empirical evidence.
Some of the rules are more like guidance for proper approaches to problems. For example in reviewing the basics in chapter 1 van Belle discusses the linear model in terms of the key assumptions of independence, equal variance and normality. The rule of thumb in section 1.4 states that assumptions should be considered in the order (1) independence, (2) equal variance and (3) normality. Van Belle explains this order by showing that the inferences are far more sensitive to violations in the independence assumption than in either the assumption of equal variance or the assumption of normality. As a statistician, I am aware of the sensitivity to correlation and the fact that variances need to differ by a factor of nearly four before results are seriously affected. Also when the data do not fit the normal distribution we have the nonparametric alternatives based on ranks. Nevertheless,in practice it is easiest and routine to test normality first, variances second and correlation becomes an afterthought. In some situations this may be okay since we may have good reason to believe that the observations were generated independently. But the rule is a good practical guidance. If you question all three assumptions it makes sense to test them in the order van Belle is suggesting.

Other practical advice of this type include the following rules of thumb:
1. Start with the Poisson to model incidence or prevalence.
2. Begin with the exponential model for time to event data.
3. Begin with two exponentials to compare two survival distributions.
4. Begin with the lognormal distribution in environmental studies.

These rules are not meant to suggest that simple models always work or even that they work in the majority of case. It is just that it is best to start simple and let the analysis and diagnostics tell you when more complicated models are needed.

This book will be a great guide for statistical practitioners and a terrific reference for professional and consulting statisticians. The references suppoting the rules are as valuable as the rules themselves.

This is a useful book for the working statistician or consultant. Many questions arise in practice that are never covered in traditional textbooks, and with experience an applied statistician learns "rules of thumb". Here is a text that nicely organizes some of the most common questions and problems and design considerations, with solid practical advice. This is not a text for a course (unless a course in consulting), but would serve an applied researcher or statistician well.

In the real world, statistical analyses are never as simple as multiple choices. There are a lot of things that have no clear cut between right and wrong. This book deals these "gray" areas. Wisdoms like the rules in this book are usually in an experienced statistician's mind. Now they are available to everyone doing statistics.

By the way, this book is so easy to read and does not have horrible equations.

A very welcome book. Packed full of useful information in a very readable form. I ordered it for the one and half page about "Overlapping confidence intervals do not imply nonsignificance". Have been overjoyed discovering the rest.

Sometimes you don't want to see a lot of "scary math notation" -- you just want to know what a concept is and how to apply it. This book meets that desire. It takes all the theoretical math concepts and explains them in very readable and accessible language so that you won't have post-traumatic flashbacks to your college statistics days.

This book provides concrete and practical advice on how to use statistical methods and how to make sure that you are using them correctly. The very first chapter (which you can read online) talks about sample size and provides simple and equations to use to design an experiment that will give you statistically valid results. That chapter is worth the purchase price itself.

Between the quality of the information, and the ease of reading, this book has got to be one of the best books out there for those who shudder at the mere mention of statistics.

A book like this is worth it if(1)you find a couple of new ideas OR (2) if you are in applied work and it's sometimes necessary to justify a rule of thumb you are using to others -- "see, I'm doing it just like they say on page XX".

By that criteria, I recommend the book. It's not a textbook and sometimes the explanations aren't as clear as I'd like, but the explanations are pretty good.

If there's a sequel, I'll probably get that as well.

This book covers an amazing number of topics. I found many important pearls and useful caveats about the tests covered. There is a rather annoying number of typos in this book, which is surprising from a major publisher. I have re-read portions of this book many times. The references are very up to date.

An incredibly useful book for all practicing applied statisticians and data analysts. All of us have heard various 'rules of thumb' and we often pass these along. This book gives a short synopsis of each rule, traces its roots, and evaluates its validity.

This is really a book for people who are working, doing data analysis, and will be most useful for people whose work involves a wide range of applications.