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Product Description
Product Description
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.
About the Author
Harvey Motulsky is also President, GraphPad Software, Inc.
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Most Helpful Customer Reviews
4 of 4 people found the following review helpful
Format:Paperback
This book is excellent. It sets out in plain english the underlying principles of statistical analysis in a clear and comprehensible manner. The coverage is at the basic level of statistical analysis, and covers the concepts of significance and confidence intervals, odds-ratios, trend tests, linear and logistic regression (no GLM's, latent class analysis, principal components, variance components etc.). The topics covered are illustrated superbly with excellent examples (including some drawn from genetics).
As a genetics statistician I have a number of people who seek assistance with statistics and if they are ever struggling with some of the basics I reccomend this book to them.
2 of 2 people found the following review helpful
Format:Paperback
Written for the researcher, and useful for those commenting on research, Prof. Motulsky explains what your statistical remarks might actually mean. Underlying assumptions are set out for every topic; warnings of common mistakes and sound advice abound. The book is written in tempting, encouraging language, the coverage is broad - up to but not including multivariate methods - and is a well presented paperback. I'm so glad that I heeded a recommendation and bought it.
1 of 1 people found the following review helpful
Format:Paperback
A really nifty book for anyone--and that's most of us--interested in what basic statistical tests mean and how to use them. Even someone with a pretty advanced knowledge of statistics may not understand all of the intuitive concepts described in this book. Consistent with its title, it is probably best suited for those in the biosciences, rather than engineers, for example, but I'd say that people in those more technical fields may very well benefit greatly from reading it. It's written in a conversational manner that is easy to digest. I'd say a lot of thinking by the author went into creating it, because he seems to guess what the reader may be thinking and then answers those questions. He's big on the confidence interval, too. Readers who need more specialized, detailed info on a particular topic, such as two-way ANOVA, probably need to consult an additional text. Overall, a great introduction to fundamental statistical concepts and tests, that will be of interest to biological scientists and many other folks, too. Author of Adjust Your Brain: A Practical Theory for Maximizing Mental Health.
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