This book is designed as a practical and intuitive introduction to probability, statistics and random quantities for physicists. The book aims at getting to the main points by a clear, hands-on exposition supported by well-illustrated and worked-out examples. A strong focus on applications in physics and other natural sciences is maintained throughout. In addition to basic concepts of random variables, distributions, expected values and statistics, the book discusses the notions of entropy, Markov processes, and fundamentals of random number generation and Monte-Carlo methods.

Simon Sirca studied physics at the Faculty of Mathematics and Physics, University of Ljubljana, and acquired his first research experience as a young researcher at the Jozef Stefan Institute in Ljubljana and the Institute for Nuclear Physics at the University of Mainz, Germany, concluding his PhD work with the thesis Axial form-factor of the nucleon from coincidence pion electroproduction at low Q². He was a postdoctoral research associate at the Massachusetts Institute of Technology and the Thomas Jefferson National Accelerator Facility (Jefferson Lab) in the USA. His main research is in the field of hadronic structure and dynamics as explored by scattering of electrons on light nuclei, exploiting state-of-the-art polarized beams, polarized targets, and techniques of recoil polarimetry. He is also involved in theoretical work on quark models of hadrons, with the focus on electroweak processes like pion electroproduction in the nucleon resonance region. He is the head of the research group Structure of Hadronic Systems that has been active in the OOPS and BLAST Collaborations at MIT, a collaboration of Jefferson Lab, and the A1 Collaboration and University of Mainz. He is an Associate Professor at the Faculty of Mathematics and Physics, University of Ljubljana, where he has been teaching numerous courses in Mathematical Physics, Modern Physics and Mathematical Physics (Computational Physics).

Basic Terminology.- Probability Distributions.- Special Continuous Probability Distributions.- Expected Values.- Special Discrete Probability Distributions.- Convolution.- Samples.- Estimation of Parameters and Statistical Tests.- Regression.- Entropy.- Markov Processes.- Generation of Pseudorandom Numbers.- Monte Carlo Method.- Stochastic Population Modeling.