This book is aimed at anyone who needs a basic introduction to statistics in the health sciences. It is based on many years’ experience teaching first‐year medical and health science students. Many of the examples are taken from primary care in the UK, which is where I worked for many years. Throughout I have tried to emphasise that medical statistics is not just a bag of tricks, and there are many synergies between its methods.

It is now over 40 years since Swinscow’s original edition of this book, and each edition reflected changes in the understanding of medical statistics. Perhaps the greatest change has occurred since the previous edition, which appeared 12 years ago. Despite the efforts of medical statisticians, there was a widespread misuse of P values, the cornerstone of conventional statistical inference. This led some journals to ban their use altogether. It is my view that used properly the P value is a useful concept, but this book, as in previous editions, concentrates on estimation rather than just hypothesis testing. The book tries to steer the reader away from an excessive devotion to P values, to instil a proper appreciation of their usefulness and to emphasise estimation over significance testing.

This book was revised during the COVID‐19 pandemic, which drew attention to the usefulness of statistics to understand public health and so there are a number of COVID‐related examples. One area where there has been much attention is the use of diagnostic tests and the relevant chapter has been considerably updated in light of the pandemic.

There have been other important changes in the statistical arena since the 11th edition. Free statistical software has become more generally available and is easier to use, particularly R with RStudio and R commander, so I have rewritten all the examples and figures in that package. All the code is given, making replication easy. The package OpenEpi remains useful and very easy to use, so I have retained some examples applying it. Computer‐intensive methods such as the bootstrap are readily understood and now easily implemented, so they are included. The links between methods are described, and this is made easier with computer‐intensive methods, which do not require specific assumptions for different methods. The formulas and worked examples are retained because without them the computer software is just a ‘black box’. The exercises on ‘playing with the data’ are also retained, since the advantage of using computers is that it is little additional effort to change the data and see the effect on the results. This kind of exercise emphasises which assumptions are important and which are less so.

This 12th edition comes with two new chapters. The first is on understanding basic numbers. This may seem somewhat elementary, but it has been my experience that many newspapers and politicians misuse basic data, to such an extent that the misuse is often accepted without comment, so I hope this chapter will provide a handy guide to scepticism on official pronouncements. I have also added a new chapter on modelling. Even new students will have to read the current literature and most papers in the health science literature now use models, so an appreciation of their use and misuse is required. For greater depth I refer the reader to a companion book to this one, Walters et al.’s Medical Statistics.¹ In addition, we have published a checklist that we hope will prove helpful for students struggling to interpret the statistics of a published paper.²

Feedback from previous editions has indicated that the Commonly Asked Questions are a useful critique of the methods. As before, each chapter contains exercises, some of which are based on the Royal College of General Practitioners’ (RCGP) Advanced Knowledge Test. There are answers to these exercises at the back of the book.

I am grateful to my colleagues Stephen Walters, Nigel Mathers and Dan Green who kindly commented on various parts of this book, to Pete Dodd who helped put the R programs on Github (https://github.com/mikejcampbell50/StatsSq1) and to Daniel Barker of the University of Newcastle, New South Wales for comments on Chapter 1. I am grateful to them and to my former colleagues Steven Julious, Richard Jacques and Dawn Teare, for support and from whom I learnt a great deal.

1. 1. Walters SJ, Campbell MJ, Machin D . Medical statistics: A textbook for the health sciences , 5th edn. Chichester: Wiley, 2020.
2. 2. Mansournia MA et al. A CHecklist for statistical Assessment of Medical Papers (the CHAMP statement): explanation and elaboration. Br J Sports Med. 2021. doi:10.1136/ bjsports‐2020‐103652.