The author introduces in an intuitive way most statistical methods currently used in the medical research literature. By means of practical examples, a good deal of historical background, and rarely a mathematical formula, the author leads the reader through both basic and more advanced concepts. The book starts from regression to the mean and Sympson’s paradox, goes through Bayesian ideas, experimental design in clinical trials, relative risks and odd ratios, all the way to case-control studies, Cox proportional hazards model, meta-analysis, and evidence based medicine. It gives the reader these, and many more, essential biostatistics concepts in a natural and intuitive way.

The text is permeated with historical references, helping to put methods and famous names into context and, more importantly, to understand how concepts evolved and how they relate to each other.

The text flows with an easiness rarely seen in statistical books. More technical sections can be skipped, so that maths adverse readers can avoid formulas altogether, with minimal prejudice to the flow.

Statisticians like me will enjoy reading it, not just for the informal treatment of our subject and extensive historical background, but also to draw on the author’s vast experience via his accessible examples. However, the main merit of the book lies in making technical concepts easy for non-mathematicians to understand. While introductory statistical books abound, few are able to make methods, drawbacks, and closely related alternatives as clear as this one. By focusing on concepts illustrated by real life examples, the author manages to make readers forget about practical technicalities, which so often hamper the good understanding of statistics by researchers and the general public. At times when statistics permeate our lives, not just through medical research but also in the general media, this clarity is very welcome.