Common Errors in Statistics (and How to Avoid Them) (4th by Phillip I. Good, James W. Hardin

By Phillip I. Good, James W. Hardin

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Amazon hyperlink: http://www.amazon.com/Common-Errors-Statistics-Avoid-Them/dp/1118294394

Praise for universal mistakes in data (and the way to keep away from Them)

"A very attractive and important booklet for all who use facts in any setting."
—CHOICE

"Addresses well known blunders frequently made in information assortment and offers an crucial consultant to actual statistical research and reporting. The authors' emphasis on cautious perform, mixed with a spotlight at the improvement of options, unearths the genuine worth of records whilst utilized accurately in any zone of research."
—MAA Reviews

Common error in statistics (and how one can keep away from Them), Fourth version offers a mathematically rigorous, but without problems obtainable beginning in data for knowledgeable readers in addition to scholars studying to layout and whole experiments, surveys, and medical trials.

Providing a constant point of coherency all through, the hugely readable Fourth version specializes in debunking renowned myths, reading universal error, and teaching readers on the right way to decide upon the perfect statistical strategy to deal with their particular job. The authors commence with an creation to the most resources of blunders and supply thoughts for fending off them. next chapters define key tools and practices for actual research, reporting, and version development. The Fourth variation positive factors newly extra themes, including:

* Baseline data
* Detecting fraud
* Linear regression as opposed to linear behavior
* Case keep watch over studies
* minimal reporting requirements
* Non-random samples
* The ebook concludes with a word list that outlines key phrases, and an intensive bibliography with a number of hundred citations directing readers to assets for extra study.

Presented in an easy-to-follow variety, universal error in data, Fourth variation is a wonderful e-book for college kids and pros in undefined, executive, medication, and the social sciences.

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Extra info for Common Errors in Statistics (and How to Avoid Them) (4th Edition)

Example text

Alas, the majority of these warnings are ill informed, 28 PART I FOUNDATIONS stressing errors that will not arise if you proceed as we recommend and place the emphasis on the why, not the what, of statistical procedures. Use statistics as a guide to decision making rather than a mandate. Neyman and Pearson [1933] first formulated the problem of hypothesis testing in terms of two types of error. Extensions and analyses of their approach are given by Lehmann [1986] and Mayo [1996]. Their approach has not gone unchallenged, as seen in Berger [2003], Berger and Berry [1988], Berger and Selke [1987], Berkson [1942], Morrison and Henkel [1970], Savage [1972], Schmidt [1996], Seidenfeld [1979], and Sterne, Smith, and Cox [2001].

The tests are being conducted in accordance with regulatory agency guidelines. Our primary hypothesis H is that the new vaccine can cut the number of infected individuals in half. From the regulatory agency’s perspective, the alternative hypothesis A1 is that the new vaccine offers no protection or, A2, no more protection than is provided by the best existing vaccine. Our task before the start of the experiment is to decide which outcomes will rule in favor of the alternative hypothesis A1 (or A2) and which in favor of the primary hypothesis H.

Our primary hypothesis H is that the new vaccine can cut the number of infected individuals in half. From the regulatory agency’s perspective, the alternative hypothesis A1 is that the new vaccine offers no protection or, A2, no more protection than is provided by the best existing vaccine. Our task before the start of the experiment is to decide which outcomes will rule in favor of the alternative hypothesis A1 (or A2) and which in favor of the primary hypothesis H. Note that neither a null nor a nil hypothesis is yet under consideration.

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