If you think that statistics has nothing to say about what you do or how you could do it better, then you are either wrong or in need of a more interesting job. Stephen Senn explains here how statistics determines many decisions about medical care--from allocating resources for health, to determining which drugs to license, to cause-and-effect in relation to disease. He tackles big themes: clinical trials and the development of medicines, life tables, vaccines and their risks or lack of them, smoking and lung cancer and even the power of prayer. He entertains with puzzles and paradoxes and covers the lives of famous statistical pioneers. By the end of the book the reader will see how reasoning with probability is essential to making rational decisions in medicine, and how and when it can guide us when faced with choices that impact our health and/or life. Stephen Senn has been a Professor of Pharmaceutical and Health Statistics at the University College of London since 1995. In 2001 he won George C. Challis Award of the University of Florida for contributions to biostatistics. Senn's previous two books are Statistical Issues in Drug Development (Wiley, 1997) and Cross-over Trials in Clinical Research (Wiley, 1993). He is the member of seven editorial boards including Statistics in Medicine and Pharmaceutical Statistics Outline of Inferential Statistical Tests and Measures of Correlation/Association -- Guidelines and Decision Tables for Selecting the Appropriate Statistical Procedure -- Inferential Statistical Tests Employed with a Single Sample -- Test 1. The Single-Sample z Test -- Test 2. The Single-Sample t Test -- Test 3. The Single-Sample Chi-Square Test for a Population Variance -- Test 4. The Single-Sample Test for Evaluating Population Skewness -- Test 5. The Single-Sample Test for Evaluating Population Kurtosis -- Test 6. The Wilcoxon Signed-Ranks Test -- Test 7. The Kolmogorov-Smirnov Goodness-of-Fit Test for a Single Sample -- Test 8. The Chi-Square Goodness-of-Fit Test -- Test 9. The Binomial Sign Test for a Single Sample