Completely rev. 2nd ed. - New York : Oxford University Press, 2010.
1 v. (various pagings) : ill. ; 24 cm.
Includes bibliographical references and index.
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
The Gaussian distribution
The lognormal distribution and geometric mean
Confidence interval of a mean
The theory of confidence intervals
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
Testing for equivalence or noninferiority
Multiple comparisons concepts
Multiple comparisons traps
Gaussian or not?
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
Simple linear regression
Multiple, logistic, and proportional hazards regression
Multiple regression traps
Analysis of variance
Multiple comparison tests after ANOVA
Sensitivity, specificity, and receiver-operator characteristic curves
Choosing a statistical test
Answers to review problems.
"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.