Chance News 18

Quotation

Single 40-year-old woman have a better chance of bing killed by a terrorist than getting married.

Too Late for Prince Charming
Newsweek, June 2, 1986

(See ""Newsweek says they were wrong" below)

Forsooths

These Forsooths are from the June 2006 RSS News.

This summer there's about a 50 per cent probability that there will be above normal temperatures for much of Britain and Europe.

The Times

5 March 2004

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To convert kilometres to miles multiply by .6214; kilometres/hour to miles/hour multiply by .6117

Schott's Almanc, page 193, Table of Conversions.

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The BBC remains just ahead of commercial radio in the UK, with a 67% share of all listeners compared with 64%.

BBC news website

2 February 2006

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How to Lie with Statistics Turns Fifty.

A review of "How to Lie with Statistics Turns Fifty"
Media Highlights, The College Mathematics Journal, Vol. 37, No 3, May 2006
Norton Starr

The College Mathematics Journal (CMJ) Media Highlights covers mathematics generally and its reviews often involve probability or statistical concepts, so Chance News readers would enjoy these reviews. As with this review, the probability and statistical contributions are usually written by Norton Starr who has been a great help to Chance News.

Here Norton reviews a special section of Statistical Science, August 2005 that recognized the 50th birthday of Darrell Huff’s famous book "How to Lie with Statistics" by asking several authors to contribute the articles for this birthday party. These articles are:

"Darrell Huff and Fifty Years of How to Lie with Statistics", Michael Steele.

"Lies, Calculations and Constructions: Beyond How to Lie with Statistics", Joel Best.

"Lying with Maps", Mark Monmonier.

"How to Confuse with Statistics or: The Use and Misuse of Conditional Probabilities", Walter Krämer and Gerd Gigerenzer.

"How to Lie with Bad Data", Richard D. De Veaux and David J. Hand.

"How to Accuse the Other Guy of Lying with Statistics", Charles Murray.

"Ephedra", Sally C. Morton.

"In Search of the Magic Lasso: The Truth About the Polygraph", Stephen, E. Fienberg and Paul C. Stern.

Norton gives a nice description of each of the papers but we (Laurie Snell) will restrict ourselves to some quotes from the articles that we found particularly interesting.

Michael Steeles tells us the story of the life of Darrell Huff and begins with:

In 1954 former Better Homes and Gardens editor

and active freelance writer Darrell Huff published a slim (142 page) volume, which over time would become the most widely read statistics book in the history of the world.

There is some irony to the world’s most famous statistics book having been written by a person with no formal training in statistics, but there is also some logic to how this came to be. Huff had a thorough training for excellence in communication, and he had an exceptional

commitment to doing things for himself.

In his article Joel Best reminds us of the failure of the "critical thinking" movement in the late 1980's and the 1990's and asks "who would teach it”. He is not very optimistic about this being done in statistics courses or in social science courses. And we were not very successful in getting people to teach our Chance course. He concludes his article with:

We all know statistical literacy is an important problem,

but we’re not going to be able to agree on its place in the curriculum. Which means that "How to Lie with Statistics" is going

to continue to be needed in the years ahead.

When we read the "The Bell Curve" by Richard Herrnstein and Charles Murray to review for Chance News, it seemed to us that the reviewers in the major newspapers could not have actually read the book. So we wrote a long review of the book for Chance News (Chance News 3.15, 3.16, 4.01).

In his article Charles Murray explains six ways to knock down a book. He discribes these as:

Tough but effective strategies for making people think that the target book is an irredeemable mess, the findings are meaningless, the author is incompetent and devious and the book’s thesis is something it isn’t.

Our experience with "The Bell Curve" made us realize that we may have seen an example of his Method 6 which he calls "THE BIG LIE" and describes as follows:

THE JUDICIOUS USE OF THE BIG LIE.

Finally, let us turn from strategies based on halftruths and misdirection to a more ambitious approach: to borrow from Goebbels, the Big Lie. The necessary and sufficient condition for a successful Big Lie is that the target book has at some point discussed a politically sensitive issue involving gender, race, class or the environment, and has treated this issue as a scientifically legitimate subject of investigation (note that the discussion need not be a long one, nor is it required that the target book takes a strong position, nor need the topic be relevant to the book’s main argument). Once this condition is met, you can restate the book’s position on this topic in a way that most people will find repugnant (e.g., women are inferior to men, blacks are inferior to whites, we don’t need to worry about the environment), and then claim that this repugnant position is what the book is about.

What makes the Big Lie so powerful is the multiplier effect you can get from the media. A television news show or a syndicated columnist is unlikely to repeat a technical criticism of the book, but a nicely framed Big Lie can be newsworthy. And remember: It’s not just the public who won’t read the target book. Hardly anybody in the media will read it either. If you can get your accusation into one important outlet, you can start a chain reaction. Others will repeat your accusation, soon it will become the conventional wisdom, and no one will remember who started it. Done right, the Big Lie can forever after define the target book in the public

mind.

Finally I agree with Norton's final remark in his review:

The articles are both a compliment to and a complement of Huff's pathbreaking venture in writing. This issue of Statistical Science is destined to be a collector's item.

Submitted by Laurie Snell

What does "unable to replicate" mean?

"Freakonomics" Author and HarperCollins Sued for Defamation, Kevin Orland, April 11, 2006 Bloomberg.com.

John Lott is an economist who has published a book "More Guns, Less Crime" that uses a multiple linear regression model to demonstrate that crime rates go own when states pass "concealed carry" laws. Concealed carry laws allow citizens to apply for the right to legally carry a concealed gun for their own protection. The regression model controlled for a large number of possible confounding variables. The theory is that if criminals do not know which of their victims might be armed, they would be more reluctant to mug strangers. This theory is very controversial and has come under attack from gun control advocates.

Steven D. Levitt and Stephen J. Dubner are economists who published a book "Freakonomics" that uses a multiple linear regression model in Chapter 4 to demonstrate that states which have a high abortion rate saw a larger drop in crime than states with a low abortion rate. The regression model controlled for a large number of possible confounding variables. The theory is that if abortion laws reduced the number of "unwanted children" fewer children would grow up in an environment of neglect and end up becoming criminals. This theory is very controversial and has under come under attack from right-to-life groups.

It is not too surprising that the authors of two such provocative regression models would end up in a public clash. Levitt and Dubner criticize Lott's research in their book, and Lott has responded by suing.

Lott said in a federal lawsuit filed yesterday in Chicago that Levitt, a University of Chicago economist, defamed him when he wrote that other scholars have been unable to replicate Lott's research linking lower crime rates with the right to carry guns. The passage amounts to an allegation that Lott falsified his results, according to the suit.

There are actually much stronger allegations about fraud concerning Lott's research. Timothy Noah, for example, published an article in Slate magazine about Lott with the title "Another firearms scholar whose dog ate his data."

But apparently, the allegation of failure to replicate is more serious.

The allegation "damages Lott's reputation in the eyes of the academic community in which he works, and in the minds of the hundreds of thousands of academics, college students, graduate students, and members of the general public who read 'Freakonomics,'" Lott said in the lawsuit.

The remedies suggested by Lott are rather harsh.

Lott's suit asks for a halt in sales, a retraction in the next printing of the book and unspecified damages from Levitt and HarperCollins.

Interestingly enough the suit does not mention the co-author, Stephen Dubner.

Questions

1. What does the phrase "unable to replicate" mean to you? Does replication mean different things in economics versus medicine? Is "unable to replicate" a code phrase used to hint that the data is fraudulent?

2. Why do you think that Lott sued Levitt and not Noah?

3. What impact might this lawsuit have on scientific criticism?

Submitted by Steve Simon

Newsweek says they were wrong

Marriage by the Numbers
Newsweek, June 6, 2006

Too Late for Prince Charming
Newsweek, June 2, 1986

The 1986 article begins with:

HIGHLIGHT:
A new study reports that college-educated women who are still single at the age of 35 have only a 5 percent chance of ever getting married

BODY:

Her sister had heard about it from a friend who had heard about it on "Phil Donahue" that morning. Her mother got the bad news via a radio talk show later that afternoon. So by the time Harvard graduate Carol Owens, 23, sat down to a family dinner in Boston, the discussion of the man shortage had reached a feverish pitch. With six unmarried daughters, Carol's said her mother was sounding an alarm. "You've got to get out of the house and meet someone," she insisted. "Now."

After two more such examples the article goes on to say:

The traumatic news came buried in an arid demographic study titled, innocently enough, "Marriage Patterns in the United States." But the dire statistics confirmed what everybody suspected all along: that many women who seem to have it all -- good looks and good jobs, advanced degrees and high salaries -- will never have mates. According to the report, white, college-educated women born in the mid-'50s who are still single at 30 have only a 20 percent chance of marrying. By the age of 35 the odds drop to 5 percent. Forty-year-olds are more likely to be killed by a terrorist: they have a minuscule 2.6 percent probability of tying the knot.

With days, that study, as it came to be known, set off a profound crisis of confidence among America's growing ranks of single women. For years bright young women single-mindedly pursued their careers, assuming that when it was time for a husband they could pencil one in. They were wrong. "Everybody was talking about it and everybody was hysterical," says Bonnie Maslin, a New York therapist. "One patient told me 'I feel like my mother's finger is wagging at me, telling me I shouldn't have waited"." Those who weren't sad got mad. The study infuriated the contentedly single, who thought they were being told their lives were worthless without a man. "I'm not a litle spinster who sits home Friday night and cries," says Boston contractor Lauren Aronson, 29. "I'm not married, but I still have a meaningful life with meaninful relationships."

On the cover of the 2006 article we see:

<font= 5>20 Years Ago
<font= 3>Newseeek Predicted a Single 40-Year-Old Woman
Had a Better Chance of Being Killed by a Terrorist
Than Getting Married. Why We Were Wrong.