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Download Intuitive Biostatistics: a Nonmathematical Guide to Statistical Thinking, 2nd Revised Edition fb2, epub

by Harvey Motulsky

Download Intuitive Biostatistics: a Nonmathematical Guide to Statistical Thinking, 2nd Revised Edition fb2, epub

ISBN: 0199730067
Author: Harvey Motulsky
Language: English
Publisher: Oxford University Press; 2nd Revised & enlarged edition (January 20, 2010)
Pages: 508
Category: Biological Sciences
Subcategory: Science
Rating: 4.8
Votes: 901
Size Fb2: 1615 kb
Size ePub: 1563 kb
Size Djvu: 1371 kb
Other formats: doc mobi lrf lrf


Individual comparisons and even a Bonferroni correction are often inadequate.

I am particularly impressed by the chapters on multiple comparisons. This is increasingly important for such molecular trickery as gene expression chips, which produce a very large number of possible comparisons. Individual comparisons and even a Bonferroni correction are often inadequate. -James F. Crow, University of Wisconsin.

This makes the text especially useful for those in health-science fields who have not taken a biostatistics course before.

oceedings{eBA, title {Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking}, author {Harvey J. Motulsky}, year {2017} }. Harvey J. Motulsky. View PDF. Save to Library.

Harvey Motulsky previously held a faculty position in the Department of Pharmacology at the University of California San Diego.

Chapter 11 explains the lognormal distribution, an essential topic omitted from many other statistics books. Harvey Motulsky previously held a faculty position in the Department of Pharmacology at the University of California San Diego. Currently, founder of GraphPad Software, Inc. Informazioni bibliografiche.

Intuitive Biostatistics. Like all statistical books, this one proves a few mathematical theorems and provides step-by-step methods for some statistical tests, but the emphasis is always on how to interpret the results, not on how to perform the tests. The book differs from other introductory biostatistics books in several ways: there is a broader range of topics - including survival curves, logistic. regression, proportional hazards regression, nonlinear regression, Bayesian analysis and lod scores.

Intuitive Biostatistics book . Motulsky also the author of the GraphPrism software for statistical analysis so that one's frequently featured (example: "I made this analysis using Graphpad. To make up for that, there's an appendix on R and Excel. Can a book be bad that features 4 or 5 XKCD comics?

Academic, Professional and General Technology Medicine & health Medicine & health. 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. Welcome to a place where words matter. On Medium, smart voices and original ideas take center stage - with no ads in sight.

THIS IS FOR THE 2nd EDITION. THE 3rd EDITION IS NOW AVAILABLE.

Comments:

Rich Vulture
I'm a retired cancer researcher (molecular biology) turned biomedical writer/editor. My use of statistics (and increasing ignorance thereof) was limited, because, in general, if an experiment didn't yield at least a 10x difference, we threw it out. Now I can't do my job without knowing biostatistics. Moltulsky's book has been enormously helpful in teaching me the basics of biostatistics and enabling me to evaluate manuscripts for correct use of statistical terms and methods. Previously I used (and still do) Motulsky's manual for his GraphPad software, the AMA Manual online, and, of course, the internet. The only problem with the book is that a digital version isn't available. However, as the author pointed out to me, you can search the book to some extent on its Amazon.com product page. Finally, the author's immediately available via email. This is a terrific resource and value!
Otrytrerl
This is a first-rate book on biostatistics. Strongly recommended for physicians and others who read and must make sense of the biomedical literature. It should also be required reading for scientists who are publishing biomedical research, preferably early in their careers when they are still impressionable. Clearly shows the strengths and limitations of statistics in interpreting re. Does a great job on the ever-present problem of multiple comparisons and retrospective data torture. Other texts are cookbooks which show you how to perform various statistical tests, but ultimately it is far more important to understand the rationale behind statistical methodology and to see what statistical tests can--and can't--tell us. Enforces the notion that statistical tests have assumptions and describes when these assumptions can be violated and when such violations render the tests worthless. Debunks the notion that "statistical significance" is an absolute concept and shows how p<0.05 is a convention, and an arbitrary one at that. Nice section on the weakness of correlation coefficients for data which violate the assumptions of that test. Strong on applying Bayesian thinking to marginal p values. Points out the important distinction between clinical and statistical significance. All in all, a great book.
Faulkree
Great book for anyone who loves statistics but isn't exactly a mathematician
Felolv
This biostatistics book is very well written with lots of examples from real experimental data. It is easy to follow and one can always go back to it as a reference when in doubt of what statistical method to use for a planned experiment. The book is very light on math. I did miss a few more equations, however that may even be a plus for people with lesser mathematical background.
showtime
I have read numerous statistics books, most of which are very poorly written. This book, however, is very well written and has many examples of interest to those who work in the medical field. This should be required reading for all biomedical researchers and physicians. For those who have a fear of statistics this book will help you overcome your fears. The author should be applauded for pointing out numerous misconception with regards to statistics and clear explanations for which statistical analysis should be used and how the results should be properly interpreted.
Bluecliff
Possibly one of the best scientific textbooks I've ever read; it really manages to deliver many important concept of biostats in a very reader-friendly format, without requiring fancy math knowledge.
There are a quite a few errors here an there, but there's a good errata online to account for them, and also a new edition has come out since I've bought it.

In my opinion, this should be read by any biology PhD or MD.

Very highly recommended
Kadar
My stats background is weak, and I have only so much time available to strengthen it. As my research develops, I need to be able to talk with statisticians and this book is helping my conceptual understanding. I keep going back to it for individual topics and plan to read it cover-to-cover.
This book presents most of the common statistical methods in a conceptual way and gives many examples from actual biomedical studies. It greatly emphasizes when a statistical method should or should not be used, and corrects some the most common misconceptions in statistics.

While the book contains some math and does quite a good job explaining it to the non-mathematician, this may not be the best book for someone who wants to deeply understand statistics from a mathematical point of view (and the author acknowledges that).

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