Introduction to Statistics and Data Analysis for Physicists

The book is addressed to students preparing Master and Ph.D. theses in particle and astrophysics but also to physicists actively working in these fields.

Apart from an introduction to standard data analysis and the required background in statistics, it introduces modern techniques like Artificial Neural Networks, bootstrap and boosted decision trees which are not covered in the standard textbooks.

A major part of the book is devoted to parameter inference (including the treatment of constrained data, distorted data, background contributions and nuisance parameters). Further subjects are Monte Carlo simulation, unfolding smeared data and hypothesis testing.

The emphasis is put on intuitive explanations which are accompanied by 171 examples and 139 figures. Auxiliary calculations, ancillary topics and mathematical details are summarized in appendices.

The book can be downloaded for private use free of charge here

A german version from 2010 with restricted content and a corresponding extension by a supplement from 2017 are available here