Data analysis methods is particularly important for the field of high-energy physics where large samples of complex data might contain tiny indications for exciting ’new physics’ which would drastically alter our picture of the world. Therefore it is important for physicists who analyse experimental data, for example from LHC, to master advanced methods and strategies of statistical data analysis. This is even more so the case as standard university education in this respect cannot always provide the broadness and depths required for successful research. This practical guide to covers the most essential statistics related tasks and problems encountered in high-energy physics data analyses, as for example parameter estimation, hypothesis testing, classification and machine learning, data unfolding and systematic uncertainties. It addresses both advanced students who recently joined the field of particle physics and researchers looking for a reliable source on optimal separation of signal and background, determining signals or estimating upper limits, correcting the data for detector effects and evaluating systematic uncertainties. The book is structured in chapters which are dedicated to a single topic and completed by a substantial number of both paper and computer exercises related to real experiments. Solutions are provided at the end of the book and an accompanying website. The book gives a review of software implementations, but does not serve as user manual for the packages. A website connected with the book provides more algorithms as well as up-to-date information and links. A special feature of this book are analysis walk-throughs to illustrate the application of the methods discussed before. The authors give examples of data analysis, referring to real problems in HEP, and display the different stages of data analysis descriptively.

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