The foundations of science are built upon centuries of careful observation. These constitute measurements that are interpreted in terms of hypotheses, models, and ultimately well-tested theories that may stand the test of time for only a few years or for centuries. In order to understand what a single measurement means we need to appreciate a diverse range of statistical methods. Without such an appreciation it would be impossible for scientific method to turn observations of nature into theories that describe the behaviour of the Universe from sub-atomic to cosmic scales. In other words science would be impracticable without statistical data analysis. The data analysis principles underpinning scientific method pervade our everyday lives, from the use of statistics we are subjected to through advertising to the smooth operation of SPAM filters that we take for granted as we read our e-mail. These methods also impact upon the wider economy, as some areas of the financial industry use data mining and other statistical techniques to predict trading performance or to perform risk analysis for insurance purposes.

This book evolved from a one-semester advanced undergraduate course on statistical data analysis for physics students at Queen Mary, University of London with the aim of covering the rudimentary techniques required for many disciplines, as well as some of the more advanced topics that can be employed when dealing with limited data samples. This has been written by a physicist with a non-specialist audience in mind. This is not a statistics book for statisticians, and references have been provided for the interested reader to refer to for more rigorous treatment of the techniques discussed here.