How to Lie with Statistics Author: Darrell Huff | Language: English | ISBN:
B00351DSX2 | Format: PDF
How to Lie with Statistics Description
Over Half a Million Copies Sold--an Honest-to-Goodness Bestseller
Darrell Huff runs the gamut of every popularly used type of statistic, probes such things as the sample study, the tabulation method, the interview technique, or the way the results are derived from the figures, and points up the countless number of dodges which are used to full rather than to inform.
- File Size: 1598 KB
- Print Length: 145 pages
- Page Numbers Source ISBN: 0393310728
- Publisher: W. W. Norton & Company; Reissue edition (December 7, 2010)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B00351DSX2
- Text-to-Speech: Not enabled
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- Lending: Not Enabled
- Amazon Best Sellers Rank: #25,794 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
- #7
in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Professional Science > Mathematics > Applied > Statistics - #7
in Kindle Store > Kindle eBooks > Nonfiction > Science > Mathematics > Applied > Probability & Statistics - #31
in Books > Science & Math > Mathematics > Applied > Statistics
- #7
in Kindle Store > Kindle eBooks > Nonfiction > Professional & Technical > Professional Science > Mathematics > Applied > Statistics - #7
in Kindle Store > Kindle eBooks > Nonfiction > Science > Mathematics > Applied > Probability & Statistics - #31
in Books > Science & Math > Mathematics > Applied > Statistics
This book, written in 1954, is just as pertinent today (perhaps even more so, as it's so easy to acquire statistics due to our current technology) -- Darrell Huff gives people the tools to talk back to statistics. Though there is a little bit about deliberate deception, in such things as "The Gee-Whiz Graph" (about how the graphical display of statistics can be twisted so that one can get any desired result, though the stats aren't changed), the meat of the book is regarding sound statistical reasoning, something that people today really need to consider.
For example, every person who listens to the latest survey showing a correlation between certain food and certain health problems or benefits should read "Post Hoc Rides Again", in which people erroneously leap from statistical correlation to a cause-and-effect relationship. An example given in the book is a report in which it was found that smokers had lower grades in college; ergo, said the researcher, smokers wishing to improve their grades should quit smoking! Of course, a statistical study showing that there's a "significant" relation between smoking and low grades doesn't show which causes the other -- perhaps educational failure draws people to smoke! My own theory would be that the =type= of person who is given to smoking is also given to not doing well in school; instead of cause and effect, one has a correlation from a shared, third (and unnamed) cause. One comes across these fallacies in the news =every=day=; I've been reading my online news, and in the science section I've already found two suspicious cause-and-effect reports. As Huff notes, it's not the statistics which are in question -- it's how they're used.
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