If you hate statistics and can't avoid learning something about them

I have just posted a review of two remarkable attempts to humanize statistics.  The books are 

Andrew Vickers (2010) What is a p-Value Anyway? 34 Stories to Help You Actually Understand Statistics. New York: Addison-Wesley, and

David Salsburg (2001) The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century. New York: Henry Holt and Company.

The review begins,

When I told our friend David Littleboy that I was having trouble understanding the statistics generated by my network analysis software, he recommended Andrew Vickers’ What is a p-Value Anyway? When I found it on Amazon, Amazon suggested that I might also want to purchase David Salsburg’s The Lady Tasting Tea. The title was irresistible. That is how I wound up buying two remarkable books. Both take on the task of humanizing statistics. They succeed in different ways....

The full review can be found here.

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Comment by John McCreery on December 14, 2011 at 6:16am

Oscar, mucho gracias. My Spanish is, alas, exceedingly rudimentary. 

Comment by Oscar González on December 14, 2011 at 5:20am

Hello John; Hello Keith:

A recent document by an anthropologist about statistics, soc-sci and more: Battles around null hypothesis in social sciences  to.ly/bJoP (in Spanish; large references/bibliography).

Have a good day,

Oscar

Comment by Keith Hart on December 12, 2011 at 10:01am

Hi John, if you could find a way of cross-posting rather than just providing a link, I am sure you will get more OAC readers and I would like them to see your review.

It's nice to see that Darrell Huff's 1954 potboiler, How to Lie with Statistics, has some contemporary replacements.

I heard a scientific explanation for the difference between milk before and after tea, but it may be phony. The first is an emulsion and the second a suspension. In any case, you can only reject the null hypothesis, not confirm it. But I guess Fisher used a simple binomial of the kind you would use to see if a coin is biased.

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