Statistical methods for data analysis explores possibilities for artificial intelligence (AI), statistics, and data science in particle physics. Although the title of the book does not mention AI, the content of the book applies some AI concepts to particle physics. Written by Professor Luca Lista, it offers an in-depth view of how statistics and data analysis can be used in particle physics. Lista is a pioneer scholar in particle physics, with many papers and citations. The book, now in its third edition, is a compilation of his lecture notes from a PhD class and various research seminars.

The book is important because, as AI and data science continue to shape the future, much interdisciplinary work is being done in many different domains. It is a very good example of interdisciplinary physics research using AI and data science.

It works as both a textbook for PhD courses and as a resource book for researchers in the applied particle physics field. Readers should have a background in basic statistics and physics. The book starts with an introduction to probability theory and basic statistics. Later chapters cover more advanced topics such as convolutions and unfolding. It also includes information on AI topics such as deep learning and convolutional neural networks (CNNs).

Graduate students are often expected to apply theoretical knowledge. This book will be an invaluable resource for them, to jumpstart their research by getting equipped with the right statistical and data analysis toolsets. The book will be too challenging to understand for the ordinary reader because of its high-level statistics and mathematics.