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.

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.

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.

This was a terrible book. To say that it is user unfriendly is an understatement. It is wordy and too long, furthermore, it doesn't teach you what you need to know. It is difficult to reference things (for example, try looking up sensitivity in the index!) and the writing is very tangential. This is not a good book if you are just starting out. I would not receommend it.

The authors write well and cover most of the important topics very thoroughly. They motivate the subject very well with a number of important and "real world" examples in the first chapter.

A unique feature is its detailed coverage of sample size determination in a number of contexts.

The book was published in 1993 which is not recent enough to cover advances in meta analysis, resampling, Bayesian Hierarchical Models (with Markov Chain Monte Carlo Methods) and frailty models. At least bootstrap methods and meta analyses are mentioned in the book.

Noteworthy are the full chapters on multiple comparison problems and discriminant analysis. This is an excellent reference book for biostatisticians.