How to Lie with Statistics Review
In his book How to Lie with Statistics Darrell Huff covers a wide range of commonly used statistics such as sample studies, interview techniques, tables, and other such samples that are derived from figures and educates the reader on how these samples are used to trick individuals rather than inform them. Huff writes to inform and entertain his readers, and he includes amusing, understandable pictures as a guide for his readers. Although the book was written in 1954, the issues Huff discusses are still prevalent today. Statistics are still used to deceive, and Huff considers statistics to be a secret language used to appeal to a fact-minded culture. I believe any one interested in a quick, informative read should consider Huff's How to Lie with Statistics. Individuals should interpret statistics used in the media and other sources with a skeptical attitude that Huff encourages. Huff provides a self-defense against these statistical tricks in his book; however, because statistical deception still exists today, I worry that the techniques he discuses have been used more for harm than good. Writers may have gained popularity, readers, or attention over the 50 years since the book's first publication by using some of the techniques Huff discloses and demonstrates. I encourage any one who encounters statistics in their lives to read his book because I believe statistical deception still occurs frequently in many publications.
Huff begins with the obvious technique of sampling. Most readers, including myself, consider themselves aware and immune to such simple deception; however, the technique is important and used frequently. I believe Huff wants his readers to first realize that these techniques may be obvious, but they are often overlooked. Any casual reader can be tricked by sampling. Huff includes an example from Time magazine. The article states that the average Yale graduate from the class of 1924 earns ,111 a year. Based on the year, this is a lot of money. After reading an article with a statistic like that, a casual and credulous reader might think Yale graduates are all making a lot of money. I am sure this type of deception occurs all the time. Readers may assume the statistics and surveys were done properly and accurately, and the numbers used are not misleading or false. This especially is the case with well-known or reputable publishers such as Time magazine. I believe Huff is proving a point in his example by using an example from Time magazine. Perhaps he is informing his readers that even well known publishers deceive their readers with statistics included within their articles. I believe readers often let their guard down when they trust a publisher or have been a loyal subscriber for many years. It is a common mistake, and Huff could have addressed this issue more specifically. The fact of the matter is that sampling simply does not give accurate results, and when a writer is trying to prove a point, he or she is not always concerned with collecting a variety of samples from the proper representative population. This is especially the case when certain samples do not represent the article's purpose.
As I previously mentioned, I worry that Huff's book explains too specially how to deceive and not how to detect deception. In his section on graphs, he explains the steps towards creating a misrepresentative graph. An individual can create a simple, uninteresting line graph such as increasing income of households over a period of time. The increase could be the result of something as normal as inflation. Huff instructs the reader to literally cut the graph in half. This split removes the excess numbers, which are not included on the line in the graph. Although this technique does not change any of the numbers, it may change the reader's interpretation of the graph. Readers may now interpret the graph to be more dramatic. A line graph may not gain extra attention from readers, but it can further emphasize a point a writer is having difficulty representing in words. This technique is not as obvious as sampling but is just as simple. I worry that Huff explains too well how this technique is done, but I understand this is the best way to explain a deceptive technique. Huff explains how to deceive frequently throughout his book. It is a good method of teaching the reader just how easy it is to deceive readers through statistics without it being obvious to the readers. Huff warns his readers about one-dimensional pictures. Any graphs that include bars which change widths and lengths while representing a single factor, are misleading to the common reader. Huff again explains by demonstrating. A simple comparison of wages of carpenters in the United States and Rotundia is all Huff needs to demonstrate bar graph deception. Huff demonstrates the effect that pictures and sizes can have on the interpretation of a bar graph. He changes the bars to moneybags and increases the width of the larger statistic. The United States' carpenters are paid dollars while a carpenter from Rotundia is paid . By doubling the width of the U.S. statistic and changing the bar to a moneybag, many readers will interpret the difference to be much bigger than twice as much. Certain topics such as line graphs cannot easily be explained without showing the process behind the deception, but perhaps the instructive methods he uses to inform his readers counteract his book's overall purpose.
Throughout the book, Huff frequently digresses. Some readers may dislike this and wish him to stay on the current topic, but I enjoyed this style. I believe it added his personality to his writing by making the writing seem less formal. He frequently reminds the readers of deceptive techniques he has already covered in his examples. For example when he uses numbers, he reminds the reader to question where and how these numbers were collected and even digress further by stating that a number such as 59.83 seems more official and representative. I believe the book was not written to be strict to its outline. Huff seems more concerned with its overall purpose, and I enjoyed this style.
Overall, Darrell Huff wants to show his readers how easy it is to deceive individuals through statistics, graphs, tables, and other figures in his book How to Lie with Statistics. He does this by giving plenty of examples and instructions. Although I worry the instructions may be too specific, they gave me a better understanding how of a certain technique is created and used. I worry for Huff that his book has been used as a guide on how to trick people with statistics, but I also know it has been informative and entertaining for me. I recommend this quick read to any one interested in gaining every-day knowledge towards interpreting articles including statistics. Huff certainly succeeds in providing a self-defense against statistics for the common reader.
How to Lie with Statistics Feature
- ISBN13: 9780393310726
- Condition: New
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How to Lie with Statistics Overview
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 fool rather than inform.
How to Lie with Statistics Specifications
"There is terror in numbers," writes Darrell Huff in
How to Lie with Statistics. And nowhere does this terror translate to blind acceptance of authority more than in the slippery world of averages, correlations, graphs, and trends. Huff sought to break through "the daze that follows the collision of statistics with the human mind" with this slim volume, first published in 1954. The book remains relevant as a wake-up call for people unaccustomed to examining the endless flow of numbers pouring from Wall Street, Madison Avenue, and everywhere else someone has an axe to grind, a point to prove, or a product to sell. "The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify," warns Huff.
Although many of the examples used in the book are charmingly dated, the cautions are timeless. Statistics are rife with opportunities for misuse, from "gee-whiz graphs" that add nonexistent drama to trends, to "results" detached from their method and meaning, to statistics' ultimate bugaboo--faulty cause-and-effect reasoning. Huff's tone is tolerant and amused, but no-nonsense. Like a lecturing father, he expects you to learn something useful from the book, and start applying it every day. Never be a sucker again, he cries!
Even if you can't find a source of demonstrable bias, allow yourself some degree of skepticism about the results as long as there is a possibility of bias somewhere. There always is.
Read How to Lie with Statistics. Whether you encounter statistics at work, at school, or in advertising, you'll remember its simple lessons. Don't be terrorized by numbers, Huff implores. "The fact is that, despite its mathematical base, statistics is as much an art as it is a science." --Therese Littleton
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