Intended for healthcare professionals


It is with trepidation that one rewrites a best seller, and Dougal Swinscow’s Statistics at Square One was one of the best selling statistical text books in the UK. It is difficult to decide how much to alter without destroying the quality that proved so popular. I chose to retain the format and structure of the original book. Most of the original examples remain; they are realistic, if not real, and tracking down the original sources to provide references would be impossible. However, I have removed the chromatic pseudonyms of the investigators. All new examples utilise real data, the source of which is referenced.
Much has changed in medical statistics since the original edition was published in 1976. Desktop computers now provide statistical facilities unimaginable then, even for mainframe enthusiasts. I think the main change has been an emphasis now on looking and plotting the data first, and on estimation rather than simple hypothesis testing. I have tried to reflect these changes in the new edition. I have found it a useful pedagogic device to pose questions to the students, and so have incorporated questions commonly asked by students or consultees at the end of each chapter. These questions cover issues often not explicitly addressed in elementary text books, such as how far one should test assumptions before proceeding with statistical tests.
I have included a number of new techniques, such as stem and leaf plots, box whisker plots, data transformation, the χ² test for trend and t test with unequal variance. I have also included a chapter on survival analysis, with the Kaplan-Meier survival curve and the log rank test, as these are now in common use. I have replaced the Kendall rank correlation coefficient by the Spearman; in spite of the theoretical advantages of the former, most statistical packages compute only the latter. The section on linear regression has been extended. I have added a final chapter on the design of studies, and would make a plea for it not to be ignored. Studies rarely fail for want of a significance test, but a flawed design may be fatal. To keep the book short I have removed some details of hand calculation.
I have assumed that the reader will not want to master a complicated statistical program, but has available a simple scientific calculator, which should cost about the same as this book. However, no serious statistical analysis should be undertaken these days without a computer. There are many good and inexpensive statistical programs. Epi-Info, for example, is produced by the Center for Disease Control (CDC) Atlanta and the World Health Organization (WHO) in Geneva. Another useful program is CIA (Confidence Interval Analysis) which is available from the BMJ.
I am most grateful to Tina Perry for secretarial help, to Simon Child for producing the figures, and to Simon Child and Tide Olayinka for help with word processing. I am particularly grateful to Paul Little, Steven Julious, Ruth Pickering and David Coggon who commented on all or part of the book, and who made many suggestions for improvement. Any errors remain my own. Finally, thanks to Deborah Reece of the BMJwho asked me to revise the book and was patient with the delays.
M J Campbell
August 1995