"There is terror in numbers," writes Darrell Huff in How to Lie with Statistics. And nowhere does this terror translate to blind acceptance of authority more than in the slippery world of averages, correlations, graphs, and trends. Huff sought to break through "the daze that follows the collision of statistics with the human mind" with this slim volume, first published in 1954. The book remains relevant as a wake-up call for people unaccustomed to examining the endless flow of numbers pouring from Wall Street, Madison Avenue, and everywhere else someone has an axe to grind, a point to prove, or a product to sell. "The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify," warns Huff.
Although many of the examples used in the book are charmingly dated, the cautions are timeless. Statistics are rife with opportunities for misuse, from "gee-whiz graphs" that add nonexistent drama to trends, to "results" detached from their method and meaning, to statistics' ultimate bugaboo--faulty cause-and-effect reasoning. Huff's tone is tolerant and amused, but no-nonsense. Like a lecturing father, he expects you to learn something useful from the book, and start applying it every day. Never be a sucker again, he cries!
Even if you can't find a source of demonstrable bias, allow yourself some degree of skepticism about the results as long as there is a possibility of bias somewhere. There always is.
Read How to Lie with Statistics. Whether you encounter statistics at work, at school, or in advertising, you'll remember its simple lessons. Don't be terrorized by numbers, Huff implores. "The fact is that, despite its mathematical base, statistics is as much an art as it is a science." --Therese Littleton

The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data. Calculus is assumed as a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus.

Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data.

'In this brilliant new edition, Andy Field has introduced important new introductory material on statistics that the student will need and was missing at least in the first edition. This book is the best blend that I know of a textbook in statistics and a manual on SPSS. It is a balanced composite of both topics, using SPSS to illustrate important statistical material and, through graphics, to make visible important approaches to data analysis. There are many places in the book where I had to laugh, and that's saying a lot for a book on statistics. His excellent style engages the reader and makes reading about statistics fun' - David C Howell, Professor Emeritus, University of Vermont, USA. This award-winning text, now fully updated with SPSS Statistics, is the only book on statistics that you will need! Fully revised and restructured, this new edition is even more accessible as it now takes students through from introductory to advanced level concepts, all the while grounding knowledge through the use of SPSS Statistics. Andy Field's humorous and self-deprecating style and the book's host of characters make the journey entertaining as well as educational. While still providing a very comprehensive collection of statistical methods, tests and procedures, and packed with examples and self-assessment tests to reinforce knowledge, the new edition now also offers: a more gentle introduction to basic-level concepts and methods for beginners; new textbook features to make the book more user-friendly for those learning about more advanced concepts, encouraging 'critical thinking'; a brand new, full-colour design, making it easy for students to navigate between topics, and to understand how to use the latest version of SPSS Statistics; both 'real world' (the bizarre and the wonderful) and invented examples that illustrate the concepts and make the techniques come alive for students; an additional chapter on multilevel modelling for advanced-level students; and, reinforced binding to make the book easier to handle at a computer workstation. The book also includes access to a brand new and improved companion Website, bursting with features including: animated 'SPSS walk-through' videos clearly demonstrating how to use the latest SPSS Statistics modules; self-marking multiple choice questions; data sets for psychology, business and management and health sciences; a flash-card glossary for testing knowledge of key concepts; and, access to support material from SAGE study skills books. Statistics lecturers are also provided with a whole range of resources and teaching aids, including: the test bank; over 300 multiple-choice questions ready to upload to WebCT, Blackboard or other virtual learning environments; charts and diagrams in electronic format for inclusion in lecture slides; and, PowerPoint slides written by the author to accompany chapters of the text.

This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master's level statistics text, this book will also give researchers an overview of research in asymptotic statistics.

For engineering statistics courses in departments of Statistics and Engineering. This text is designed for a two-semester introductory course in statistics for students majoring in engineering or any of the physical sciences. Inevitalby, once these studenrts graduate and are employed, they will be involved in the collection and analysis of data and will be required to think critically about the results. Consequently, they need to acquire knowledge of the basic concepts of data description and statistical inference and familiarity with statistical methods they are required to use on the job.The text includes optional theoretical exercises allowing instructors who choose to emphasize theory to do so without requiring additional materials. The assumed mathematical background is a two-semester sequence in calculus - that is, the course could be taught to students of average mathematical talent and with a basic understanding of the principles of differential and integral calculus. Datasets and other resources (where applicable) for this book are available here.