Intuitive Biostatistics: A Nonmathematical Guide to Statistical ThinkingThoroughly 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 multipart problems, with answers fully discussed in Chapter 48. * New Q and A sections throughout the book review key concepts. 
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Review: Intuitive Biostatistics, First Edition
User Review  Alexander Shearer  GoodreadsAn excellent introduction to a wide breadth of statistics with a focus on (1) practical application and (2) the true theoretical basis and not (3) a bunch of math problems. Read full review
Review: Intuitive Biostatistics, First Edition
User Review  George  GoodreadsMy old KZSU friend Sam Wang suggested this as a fun read. I look forward to seeing how compares to two favorite by Edward Tufte, Envisioning Information and The Visual Display of Quantitative Information, from my days teaching technical communication and writing to engineers at Stanford. Read full review
Contents
PREFACE  
Introducing Statistics  
Confidence Intervals  
Continuous Variables  
P Values and Significance  
Challenges in Statistics  
Statistical Tests  
Fitting Models to Data  
The Rest of Statistics  
Putting It All Together  
APPENDICES  
Common terms and phrases
analysis analyze answer assume assumption average bestfit value calculate case–control study cells censored chance Chapter computed conclusion correlation coefficient count data sets defined difference disease drug effect equal equation error bars example expect experiment experimental explained Figure fraction Gaussian distribution graph hypothesis is true Ifthe Ifyou independent variables interpret investigators larger linear regression logarithms logistic regression lognormal distribution measured method multiple comparison tests multiple comparisons multiple regression nonlinear regression nonparametric tests normality test null hypothesis number of subjects observed odds ratio ofthe onetail P value oneway ANOVA outcome outliers paired parameters patients plots Poisson distribution population mean predicted probability quantify randomly range receptors relative risk sample mean scatter shows significance level simulated slope standard error statistical hypothesis testing statistically significant sum of squares survival curves Table tion treatment twotail P value Type II error unpaired t test zero