Best Biostatistics Books of 2025

The Book of Why by Judea Pearl, a Turing Award-winning computer scientist and statistician, is a groundbreaking work that explores the study of cause and effect on a scientific basis. Pearl's work not only explains how we can determine whether something causes another, but also enables us to explore the worlds that could have been. The book provides valuable lessons on the history of ideas and the conceptual tools needed to judge what big data can and cannot deliver. This book is a must-read for anyone interested in understanding causality, human thought, and artificial intelligence.

Tweak: Growing Up on Methamphetamines is a raw and honest memoir of Nic Sheff's addiction to methamphetamine. Sheff paints a harrowing portrait of a person at odds with his past, family, substances, and himself. The book is a compelling, heartbreaking, and true story of his relapse and the road to recovery. Sheff's writing style is terse, honest, and spontaneous, making it difficult to put down. It's a must-read for every teenager and every parent of one, as it offers insight into the dangerous world of addiction. Overall, Tweak is a riveting, brilliantly written, and searingly honest memoir that is both essential and unique.

The Kaggle Book is an invaluable guide for data enthusiasts looking to improve their analysis skills and compete in Kaggle competitions. With insights from over 30 Kaggle Masters and Grandmasters, this book covers everything from modeling strategies to data handling techniques. It also includes general approaches for tasks based on image, tabular, textual data, and reinforcement learning. The book's unique selling point is its comprehensive coverage of Kaggle-specific tips and tricks that aren't easily found elsewhere. Overall, this book is a must-read for anyone looking to improve their data science skills and succeed in Kaggle competitions.

Deep Medicine by Eric Topol is a compelling read that explores the potential of artificial intelligence in revolutionizing patient care. The book highlights the broken doctor-patient relationship and how AI can transform everything from diagnosis to treatment, reducing costs and human mortality. Topol's vision is optimistic, and he argues that AI can save time, lives, and money. The book is enlightening and provides a gimlet-eyed look at the role of computers in medicine. Overall, Deep Medicine is a unique and important account that should be required reading for medical practitioners and the public alike.

Statistics for Laboratory Scientists and Clinicians is a concise and practical guide that takes researchers through the process of selecting and implementing appropriate statistical methods to analyze results. With practical examples and clear explanations, Anne McDonnell Sill makes complex statistical concepts easy to understand. Her writing is straightforward and accessible, making this book an excellent reference for researchers who want to achieve statistical autonomy. Overall, this is an excellent addition to the Medical Diagnosis Books category, and its practical approach sets it apart from other statistics textbooks.

Basic Biostatistics: Statistics for Public Health Practice is a comprehensive, introductory text that covers the fundamental principles of biostatistics and focuses on the common types of data encountered in public health and biomedical fields. The book is organized into three parts, covering basic concepts and techniques, analytic techniques for quantitative response variables, and techniques for categorical responses. The Second Edition includes many new exercises and provides access to a comprehensive companion website with student and instructor resources. With language, examples, and exercises that are accessible to students with modest mathematical backgrounds, this is the perfect introductory biostatistics text for undergraduates and graduates in various fields of public health.

Nursing Informatics: Scope and Standards of Practice, 3rd Edition is a comprehensive guide to the nursing informatics specialty, providing national standards of practice and performance. It is a valuable reference for quality improvement initiatives, certification, and credentialing, position descriptions and performance appraisals, classroom teaching, and in-service education programs. The book is intended to guide nurses, administrators, legislators, regulators, legal counsel, and other interprofessional colleagues. Overall, this book is a must-read for anyone interested in nursing informatics and is an excellent resource for those seeking to improve their knowledge and skills in this field.

Health Informatics: An Interprofessional Approach, 2nd Edition is a concise and informative guide to the intersection of information technology and healthcare. This book covers a range of topics, including electronic health records, telehealth, and social media tools, as well as new topics such as data science and analytics. The book also includes case studies and discussion questions to encourage critical thinking. Overall, this book is a valuable resource for healthcare professionals looking to stay up-to-date on the evolving practice of health informatics.

Clinical Biostatistics and Epidemiology Made Ridiculously Simple is a concise and intuitive guide to understanding some of the most important concepts in clinical biostatistics. The book emphasizes concepts over math and uses clinical examples to help readers interpret medical research reports. This book provides a clear approach to terminology, research design, and various statistical testing. It is an ideal resource for healthcare students and professionals looking to master medical knowledge in a simple and effective way. Overall, this book is an excellent resource for those looking to gain a deeper understanding of biostatistics and epidemiology.

Designing Clinical Research is a comprehensive guide for healthcare practitioners involved in clinical and public health research. The book provides practical and up-to-date approaches to designing, funding, and implementing a study. The fifth edition features new figures, tables, and design, as well as new editors, new content, and extensively updated references. It covers clinical research in its many forms, including clinical trials, observational studies, translational science, and patient-oriented research. The book also discusses key changes in the field, such as confounding, surrogate outcomes, and machine learning. The addition of new chapters on qualitative approaches to clinical research and community-engaged research makes this book unique and valuable for researchers.