This book was written because I could not find a suitable textbook to use as the basis of an undergraduate course on scientific inference, statistics and data analysis. Although there are good books on different aspects of introductory statistics, those intended for physicists seem to target a post-graduate audience and cover either too much material or too much detail for an undergraduate-level first course. By contrast, the ‘Intro to stats’ books aimed at a broader audience (e.g. biologists, social scientists, medics) tend to cover topics that are not so directly applicable for physical scientists. And the books aimed at mathematics students are usually written in a style that is inaccessible to most physics students, or in a recipe-book style (aimed at science students) that provides ready-made solutions to common problems but develops little understanding along the way.

This book is different. It focuses on explaining and developing the practice and understanding of basic statistical analysis, concentrating on a few core ideas that underpin statistical and data analysis, such as the visual display of information, modelling using the likelihood function, and simulating random data. Key concepts are developed using several approaches: verbal exposition in the main text, graphical explanations, case studies drawn from some of history's great physics experiments, and example computer code to perform the necessary calculations. The result is that, after following all these approaches, the student should both understand the ideas behind statistical methods and have experience in applying them in practice.