"Thoroughly revised and updated, the second edition of Intuitive Biostatistics retains and refines the core perspectives of the previous edition: a focus on how to interpret statistical results rather than on how to analyze data, minimal use of equations, and a detailed review of assumptions and common mistakes. Intuitive Biostatistics, Completely Revised Second Edition, provides a clear introduction to statistics for undergraduate and graduate students and also serves as a statistics refresher for working scientists. New to this edition: Chapter 1 shows how our intuitions lead us to misinterpret data, thus explaining the need for statistical rigor. Chapter 11 explains the lognormal distribution, an essential topic omitted from many other statistics books. Chapter 21 contrasts testing for equivalence with testing for differences. Chapters 22, 23, and 40 explore the pervasive problem of multiple comparisons. Chapters 24 and 25 review testing for normality and outliers. Chapter 35 shows how statistical hypothesis testing can be understood as comparing the fits of alternative models. Chapters 37 and 38 provide a brief introduction to multiple, logistic, and proportional hazards regression. Chapter 46 reviews one example in great depth, reviewing numerous statistical concepts and identifying common mistakes. Chapter 47 includes 49 multi-part problems, with answers fully discussed in Chapter 48. New "Q and A" sections throughout the book review key concepts"--Provided by publisher.; OverviewIntuitive Biostatistics is both an introduction and review of statistics. Compared to other books, it has:Breadth rather than depth. It is a guidebook, not a cookbook.Words rather than math. It has few equations.Explanations rather than recipes. This book presents few details of statistical methods and only a few tables required to complete the calculations.Who is it for?I wrote Intuitive Biostatistics for three audiences:Medical (and other) professionals who want to understand the statistical portions of journals they read. These readers don\'t need to analyze any data, but need to understand analyses published by others.Undergraduate and graduate students, post-docs and researchers who will analyze data. This book explains general principles of data analysis, but it won\'t teach you how to do statistical calculations or how to use any particular statistical program. Scientists who consult with statisticians. Statistics often seems like a foreign language, and this text can serve as a phrase book to bridge the gap between scientists and statisticians.What\'s new in the second edition?Though the spirit of the first edition remains, very few of its words do. It is hard to explain what is new in this edition, since I essentially rewrote the entire book. New and expanded topics in the second edition of Intuitive Biostatistics include:Chapter 1 explains how our intuitions can lead us astray in issues of probability and statistics.Chapter 11 (and later examples) highlight the fact that lognormal distributions are common.Chapter 21 explains the idea of testing for equivalence vs. testing for differences. Chapters 22, 23, and 40 discuss the pervasive problem of multiple comparisons. Chapters 24 and 25 discuss testing for normality and for outliers.Chapter 35 shows how to think about statistical hypothesis testing as comparing the fits of alternative models.Chapters 37 and 38 give expanded coverage of the usefulness--and traps--of multiple, logistic, and proportional hazards regression.Chapter 43 briefly mentions adaptive study designs where sample size is not chosen in advance.Chapter 46 (inspired by, and written with, Bill Greco) reviews many topics in this book and more general issues of how to approach data analysis.

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Statistics and probability are not intuitive -- Why statistics can be hard to learn -- From sample to population -- Confidence interval of a proportion -- Confidence interval of survival data -- Confidence interval of counted data -- Graphing continuous data -- Types of variables -- Quantifying scatter -- The Gaussian distribution -- The lognormal distribution and geometric mean -- Confidence interval of a mean -- The theory of confidence intervals -- Error bars -- Introducing P values -- Statistical significance and hypothesis testing -- Relationship between confidence intervals and statistical significance -- Interpreting a result that is statistically significant -- Interpreting a result that is not statistically significant -- Statistical power -- Testing for equivalence or noninferiority -- Multiple comparisons concepts -- Multiple comparisons traps -- Gaussian or not? -- Outliers -- Comparing observed and expected distributions -- Comparing proportions : prospective and experimental studies -- Comparing proportions : case-controlled studies -- Comparing survival curves -- Comparing two means : unpaired t-test -- Comparing two paired groups -- Correlation -- Simple linear regression -- Introducing models -- Comparing models -- Nonlinear regression -- Multiple, logistic, and proportional hazards regression -- Multiple regression traps -- Analysis of variance -- Multiple comparison tests after ANOVA -- Nonparametric methods -- Sensitivity, specificity, and receiver-operator characteristic curves -- Sample size -- Statistical advice -- Choosing a statistical test -- Capstone example -- Review problems -- Answers to review problems.