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And most of the examples and 10 e pill are fairly timeless and universal. Another subtle problem emerges in Huff's citations of Alfred Kinsey pgs. Readers aren't likely to see the problem unless they know some backstory. Huff approvingly cites the witn research of Alfred Kinsey.

Kinsey, if you didn't know, was a scientist whose now famous studies into human sexuality would play a major role, academically, in fueling the sexual revolution.

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Huff does not mention sex or anything from Kinsey's work that would be inappropriate for younger audiences. Now Huff could not have known at the time that Kinsey's core studies into human sexuality have since been widely discredited for many of the same sampling errors Why is it so easy to lie with statistics Huff criticizes in this book. For example, Kinsey drew a disproportionate amount of his survey data from prison populations, even though the subject matter was about romantic relations, and he presented his findings as if they represent the general population.

Obviously, male prisoners will find their romantic options restricted if they aren't allowed near women. That factor taints the sample, so Kinsey wasn't justified in applying his results to behavior in the general population.

Huff, however, has an excuse.

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Unfortunately, some sources today commit the same oversight, perhaps more concerned about Kinsey's influence than his accuracy. Naturally, most readers will be oblivious to the Kinsey problem.

And in sum, this quibble is immaterial to the overall value of the book. Having covered the summary and critique of this book, a practical question remains: How can educators use this book? How to Lie with Statistics is a helpful, though sparse, introductory level supplement for use in classes on statistics, mathematics, critical thinking, and logic.

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It would also be yo for marketing and advertising students sl want to understand some of the implicit ethics involved Baltimore single bbw selling a product.

Teachers and students could easily spend a brief time, maybe minutes, discussing what a given chapter is about, interact with some examples, and then move on to a different textbook or lesson material. This book is not an academic textbook, or a workbook, but each chapter is discrete so that it could stand alone, and the subject matter easily lends itself to sample problems.

Why is it so easy to lie with statistics

A Speed dating olsztyn opinie glance offers an easy-to-read s accurate sense of things. Looking for a little more shock value? Chop off the bottom of that same graph, don't start from zero, and, voila, the exact same graph looks much more extreme. Or, throw caution to the wind and change the proportions between the ordinate x and the abscissa y. There are no rules to these things and the graph suddenly has a very different message indeed.

Numbers that slope comfortably now thunder upward like thoroughbreds on Derby Day.

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The terrific example Huff offers in the book: It is safer to drive at 7am than at 7pm, because four times more accidents occur at the latter time. The problem?

The relevant fact is not the time of day, it is the fact that there are more drivers out on the road at the later time. More drivers, more miles covered, more time and distance in which an accident might occur.

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For example, chance - pure and simple - might give you the results you need to say what you want to say. Two utterly unrelated events occur by chance, and the result works and the undiscerning eye will never know otherwise.

Or, two related events happen, you just aren't sure which is the cause, and which the effect.

A chicken-egg quandary that allows you to dub one the cause, the other effect, as suits your purpose. Or, you would like to conclude what goes beyond the scope of the date. It rains, crops grow.

Rain is always good for crops. Well, except too much rain is not good for crops. If you'd like to stop with 'rain is good,' there is nothing stopping you.

How to Lie With Statistics by Darrell Huff—A Review | The Quad Magazine

The trickiest of them all, the one most often used to spurious effect: when two events have zero effect on the other, yet there is a very real correlation between the two. Duff offers this example: Suicide rates are highest in June. Do suicides produce June brides?

Or, do June weddings produce suicidal urges?

Or, unrelated and more likely the case, the very depressed soul who gets makes it through the winter with Why is it so easy to lie with statistics promise of a better spring, finds that hasn't been the case, proceeds to the sad, next step. Click here to buy the book. World globe An icon of the world globe, indicating different international options.

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Circular analysis Correlation does not imply causation French erotic sex to Lie with Statistics Impression management " Lies, damned lies, and statistics " Misleading graph Sampling bias. Categories : books Statistics books Mathematics books Misuse of statistics. Namespaces Article Talk. Views Read Edit View history. In other projects Wikiquote.

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First edition.