The third edition of PDQ Statistics provides an overview of all major statistical methods, giving the reader a good understanding of statistics and how they are used in research articles. It covers the major categories – variable and descriptive statistics, parametric statistics, non-parametric statistics, and multivariate statistics. The explanations are clear, succinct, and loaded with practical examples.

PDQ Epidemiology will help to fill the considerable gap between the exact findings of the laboratory and the uncertain world of clinical medicine. By translating the terminology of epidemiology into easy-to-understand language, the underlying scientific methods begin to emerge and make sense. The third edition reflects the maturation of the field, which now encompasses much more than the 'randomized, controlled trial.' New sections explain techniques that have been introduced into the field since the previous edition, the section on RCT has been expanded and updated examples have been incorporated. The section on measurement has also been brought into line with newer thinking on reliability and validity. Featuring unique examples titled Circular Reasoning and Anti-intellectual Pomposity Detectors (CRAP Detectors), the text helps the reader identify studies with basic flaws in design or reasoning.

Provides instructions on developing optimal database search strategies for retrieving sound clinical studies of etiology, prognosis, diagnosis, prevention or treatment of disorders.

This new edition of the book will be produced in two versions. The textbook will include a CD-Rom with two videotaped lectures by the authors. This book translates biostatistics in the health sciences literature with clarity and irreverence. Students and practitioners alike, applaud Biostatistics as the practical guide that exposes them to every statistical test they may encounter, with careful conceptual explanations and a minimum of algebra. What's New? The new Bare Essentials reflects recent advances in statistics, as well as time-honored methods. For example, "hierarchical linear modeling" which first appeared in psychology journals and only now is described in medical literature. Also new, is a chapter on testing for equivalence and non-inferiority. As well as a chapter with information to get started with the computer statistics program, SPSS. Free of calculations and jargon, Bare Essentials speaks so plainly that you won't need a technical dictionary. No math, all concepts. The objective is to enable you to determine if the research results are applicable to your own patients. Throughout the guide, you'll find highlights of areas in which researchers misuse or misinterpret statistical tests. We have labeled these "C.R.A.P. Detectors" (Convoluted Reasoning and Anti-intellectual Pomposity), which help you to identify faulty methodology and misuse of statistics.

Introductory Statistics 2e provides an engaging, practical, and thorough overview of the core concepts and skills taught in most one-semester statistics courses. The text focuses on diverse applications from a variety of fields and societal contexts, including business, healthcare, sciences, sociology, political science, computing, and several others. The material supports students with conceptual narratives, detailed step-by-step examples, and a wealth of illustrations, as well as collaborative exercises, technology integration problems, and statistics labs. The text assumes some knowledge of intermediate algebra, and includes thousands of problems and exercises that offer instructors and students ample opportunity to explore and reinforce useful statistical skills. This is an adaptation of Introductory Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License.

Intended to be a quick access guide to statistical concepts and formulae aimed at healthcare professionals and researchers and to help provide information for learning how to critically evaluate medical literature and research. The book aims to be factual whilst taking a humorous overview.

Vital Statistics: an introduction to health science statistics e-book is a new Australian publication. This textbook draws on real world, health-related and local examples, with a broad appeal to the Health Sciences student. It demonstrates how an understanding of statistics is useful in the real world, as well as in statistics exams. Vital Statistics: an introduction to health science statistics e-book is a relatively easy-to-read book that will painlessly introduce or re-introduce you to the statistical basics before guiding you through more demanding statistical challenges. Written in recognition of Health Sciences courses which require knowledge of statistical literacy, this book guides the reader to an understanding of why, as well as how and when to use statistics. It explores: - How data relates to information, and how information relates to knowledge - How to use statistics to distinguish information from disinformation - The importance of probability, in statistics and in life - That inferential statistics allow us to infer from samples to populations, and how useful such inferences can be - How to appropriately apply and interpret statistical measures of difference and association - How qualitative and quantitative methods differ, and when it's appropriate to use each - The special statistical needs of the health sciences, and some especially health science relevant statistics - The vital importance of computers in the statistical analysis of data, and gives an overview of the most commonly used analyses - Real-life local examples of health statistics are presented, e.g. A study conducted at the Department of Obstetrics and Gynecology, University of Utah School of Medicine, explored whether there might be a systematic bias affecting the results of genetic specimen tests, which could affect their generalizability. - Reader-friendly writing style - t-tests/ ANOVA family of inferential statistics all use variants of the same basic formula - Learning Objectives at the start of each chapter and Quick Reference Summaries at the end of each chapter provide the reader with a scope of the content within each chapter.