Intuitive Biostatistics 4th Edition – Harvey Motulsky

All you can say is that there is no strong ­evidence that the value came from a different distribution. How do outlier tests work? Statisticians have devised several methods for detecting outliers. All of the methods first quantify how far the outlier is from the other values. This distance can be the difference between the extreme value and the mean of all values, the difference between the extreme value and the mean of the remaining values, or the difference between the extreme value and the next closest value.

This value is then standard- ized by dividing by some measure of variability, such as the SD of all values, the SD of the remaining values, the distance to the closest value, or the range of the data. To determine whether the extreme value can be considered a statistically significant outlier, the calculated ratio is compared with a table of critical values. One of the most popular outlier tests is the Grubbs outlier test (also called the extreme studentized deviate test).

This test divides the difference between the extreme value and the mean of all values by the SD of all values. Some people feel that removing outliers is cheating. It can be viewed that way when outliers are removed in an ad hoc manner, especially when you remove only those outliers that get in the way of obtaining results you like. But leaving outli- ers in the data you analyze can also be considered cheating, because it can lead to invalid results.

It is not cheating when the decision of whether to remove an outlier is based on rules and methods established before the data were collected and these rules (and the number of outliers removed) are reported when the data are published. When your experiment has a value flagged as an outlier, it is possible that a coincidence occurred, the kind of coincidence that happens in 5% (or whatever level you pick) of experiments even if the entire scatter is Gaussian.

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CIP data is on file at the Library of Congress 978-0-19-064356-0 987654321 Printed by LSC Communications, Inc. United States of America I dedicate this book to my wife, Lisa, to my kids (Wendy, Nat, Joey, and Ruby), to readers who encouraged me to continue with a fourth edition, and to future scientists who I hope will avoid common mistakes in biostatistics.  PRAISE FOR INTUITIVE BIOSTATISTICS  Intuitive Biostatistics is a beautiful book that has much to teach experimental bi- ologists of all stripes.

Unlike other statistics texts I have seen, it includes extensive and carefully crafted discussions of the perils of multiple comparisons, warnings about common and avoidable mistakes in data analysis, a review of the assump- tions that apply to various tests, an emphasis on confidence intervals rather than P values, explanations as to why the concept of statistical significance is rarely needed in scientific work, and a clear explanation of nonlinear regression (com- monly used in labs; rarely explained in statistics books).

In fact, I am so pleased with Intuitive Biostatistics that I decided to make it the reference of choice for my postdoctoral associates and graduate students, all of whom depend on statistics and most of whom need a closer awareness of pre- cisely why. Motulsky has written thoughtfully, with compelling logic and wit. He teaches by example what one may expect of statistical methods and, perhaps just as important, what one may not expect of them.

He is to be congratulated for this work, which will surely be valuable and perhaps even transformative for many of the scientists who read it.

This is a short excerpt from the opening of “” by Unknown, quoted for review and introduction purposes. All rights belong to the copyright holders.

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