T D V Swinscow, M J Campbell. (Pp 158; £11.95). BMJ Books, 2002. ISBN 0-7279-1552-5.

This book provides a nice overview of many basic statistical methods. It is clearly focused to be used by, for example, epidemiologists who need some statistical guidance. Topics that are discussed include: descriptive statistics, testing of hypotheses/construction of confidence intervals both for continuous as well as binary data, non-parametric statistics, linear regression, survival analysis.

There is a big change in this 10th edition, compared with the previous editions: because almost everyone has access to a computer nowadays, many details on the calculations for a pocket calculator have been removed. Instead, reference is made to easy software packages, even freeware if available. As a result of this, exact statements on p values are made, instead of, like in the previous editions, giving boundaries based on the distribution tables. Luckily, these tables are still kept at the end of the book.

In each chapter, an interesting section on reading and reporting statistics in the medical literature has been added. Another nice expansion in this new edition is made in the section on binary data, where summary statistics like relative risk and odds ratio are included.

The book touches many subjects, and describes them very concisely. This is convenient if you want to look up a method and apply it. But if you want to know many details on a technique, you better look into one of the references. It is good that not only formulas and calculations are given, but that also some explanation is given on related issues like trial set up or sample size.

At the end of each chapter, there is a section called “common questions”. These are very pleasant to read, and they provide short answers to typical questions. The only drawback is that these answers are in many cases not reflected in the body of the chapter.

It is a short book, covering many statistical techniques. I have very much enjoyed reading it. The book is too concise if you want to study techniques in depth, but ideal to learn how to apply all the statistical theory that is covered.