Chance News 62

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"It is a very sad thing that nowadays there is so little useless information."
--Oscar Wilde

Quoted in All too much: Monstrous amounts of data
The Economist, 25 February 2010

"Statisticians are engaged in an exhausting but exhilarating struggle with the biggest challenge that philosophy makes to science: how do we translate information into knowledge? ...

"If you think that statistics has nothing to say about what you do or how you could do it better, then you are either wrong or in need of a more interesting job."

--Stephen Senn, Dicing With Death

Suggested by Paul Alper

"Of course, there are plenty of veteran basketball decision-makers who don't believe that stats are the only key to success. They'll tell you the difference in winning percentages has less to do with statistical analysis than with statistical freaks. 'If you took LeBron off the Cavaliers, you could give them 10,000 number crunchers, and it wouldn't make a difference,' says [one team executive]."
--David Biderman, “Are Statheads the NBA’s Secret Weapon?”, The Wall Street Journal, March 12, 2010

This brief article indicates that the 15 NBA teams with data analysts have won 59% of their combined games this season, while the 15 teams without data analysts have won only 41% of their games.

Submitted by Margaret Cibes


Chance moves to CAUSEweb

Chance Project Moves to CAUSEweb
Amstat News, 1 March 2010

This story announces that the Chance materials will be moving to a new home at CAUSEweb, effective March 15. Actually, the Chance News Wiki quietly moved a week earlier--you will see a new URL in your browser! If (by chance) you didn't notice, it was because of the wonderful work by the technical experts at Dartmouth College and CAUSEweb, who enabled a very smooth transition and redirect of links from the Dartmouth site.

Submitted by Bill Peterson

Media highlights

The College Mathematics Journal has a column called
"Media Highlights". Norton Starr is one of the editors and
his contributions are usually of interest to Chance News
readers. The March issue has two such items.

(1) For Decades, Puzzling People With Mathematics
The New York Times Stevin D Levitt
March 11, 2010

Norton writes:

This article gives an inspiring portrait of Martin Gardner, the

premier exponent of recreational mathematics for over 50 years.
It invites readers to see mathematics vastly richer and more interesting
than they may recall from their classrooms exercise. Norton says more

about Martin and ends with a quote from Rohnald Graham:
Martin has turned thousands of children into mathematicians,
and thousand of mathematicians into children.

From this article we also learn that Martin Gardner recently had his 95th birthday and is still publishing new books.

(2) Do We Need a 37-Cent Coin?
The New York Times John Tierney
October 19,2009
This is a article from a New York Times Blog called Freakonomics and maintained by John Tierney.

Here Norton Writes:

This article reports on the work of economist Patrick DeJarnette, who developed some unusual results in probabilistic integer arithmetic. We can combine pennies, nickels, dimes and quarters to pay for any item costing below a dollar (freebies included, so (0.99 cents) is the relevant range). With the assumption that each of these 100 prices is equally likely to occur and that a purchaser uses the fewest possible coins, then on average each purchase requires 4.70 coins. DeJarnette asked if some other set of four coins, perhaps including one worth 8 or 61 or 37 cents would yield a lower average. He found that the optimal result uses an average of 4.10 coins per purchase, and is achieved by the sets (1, 3, 11, 37,) or (1, 3, 11, 38).

Norton concludes his discussion witht "there is a lot of play in this curious study." This is verified by looking at the 116 comments at the end of this Blog.

Submitted by Laurie Snell

Back of the envelope calculations about Toyota

Toyotas Are Safe (Enough). Robert Wright, The New York Times Blog, March 9, 2010.

How worried are you about driving a Toyota? Robert Wright is not that worried.

My back-of-the-envelope calculations (explained in a footnote below) suggest that if you drive one of the Toyotas recalled for acceleration problems and don’t bother to comply with the recall, your chances of being involved in a fatal accident over the next two years because of the unfixed problem are a bit worse than one in a million — 2.8 in a million, to be more exact. Meanwhile, your chances of being killed in a car accident during the next two years just by virtue of being an American are one in 5,244. So driving one of these suspect Toyotas raises your chances of dying in a car crash over the next two years from .01907 percent (that’s 19 one-thousandths of 1 percent, when rounded off) to .01935 percent (also 19 one-thousandths of one percent).

Of course, the type of risk involved is part of the problem.

But lots of Americans seem to disagree with me. Why? I think one reason is that not all deaths are created equal. A fatal brake failure is scary, but not as scary as your car seizing control of itself and taking you on a harrowing death ride. It’s almost as if the car is a living, malicious being.

Robert Wright includes an appendix with all of the computations and assumptions that went into these numbers.

Submitted by Steve Simon


1. People seem to make a distinctions between risks that they place upon themselves (e.g., talking on a cell phone while driving) and risks that are imposed upon them by an outsider (e.g., accidents caused by faulty manufacturing). Is this fair?

2. Contrast the absolute change in risk (.01935-.01907=.00028) with the relative change in risk (.01935/.01907=1.0147). Which way seems to better reflect the change in risk?

3. Examine the assumptions that Robert Wright uses. Do these seem reasonable?

Placebos getting stronger?

The growing power of the sugar pill
by Alix Spiegel, NPR, 8 March 2010

The randomized double-blind placebo-controlled experiment is regarded as the "gold standard" in medical research. This story describes new research indicating that our response to placebo treatments may be getting stronger over time.

To be continued...

Submitted Bill Peterson

Ranking Olympics countries: The first may be last

“Not All Countries Are Rich and Snowbound”
by David Biderman, The Wall Street Journal, February 18, 2010

WSJ staff came up with an alternative system for ranking a country’s success in the Olympics, based on eight factors that they felt could affect a country’s performance. They then analyzed and ranked ten Winter Olympics 2010 countries as of February 16, based on the factors of population, per-capita GDP, average temperature, infant mortality rate, per-capita number of cars bought per month, percent of smokers, per-capita daily protein consumption, and per-capita yearly alcohol consumption.

A chart, “Another Way to Measure Olympic Aptitude”, shows the data for the eight factors, the medal counts, and the WSJ scores for the ten countries.

Italy came out on top, with 3 medals and a WSJ score of 0.942, while Germany came in last with 9 medals and a WSJ score of 0.750.

Submitted by Margaret Cibes

Athletes' birthdays

“Born Late Year? Choose Another Sport”
by David Biderman, The Wall Street Journal, March 21, 2010

A researcher at Queensland University of Technology compared the distribution of birthdays of the 617 Australian-born Australian Football League 2009 players in particular, to Australian birth statistics in general.

He found the following quarterly data on expected number of birthdays, actual number of birthdays, and percent more/less than expected :

Jan-Mar: 153, 196, +28.1%

Apr-Jun: 155, 162, +4.5%
Jul-Sep: 157, 137, -12.7%

Oct-Dec: 152, 122, -19.7%

He suggested that the decreasing percent changes could be explained by the January 1 cut-off date for youth-sports leagues, which “means players born near or after that date grow up with physical advantages over their competitors, likely influencing them to keep playing.” (Biderman reports that other studies have found similar results in European soccer and Canadian hockey.)


1. If Australian birthdays, in general, were uniformly distributed in any year, how many AFL player birthdays would you have expected in each quarter of a leap year? Of a non-leap year?

2. In what way(s) does the shape of either quarterly distribution in question 1 differ from the researcher’s quarterly distribution of expected AFL player birthdays?

3. How do you think the researcher calculated the expected number of AFL player birthdays in each quarter? What, if anything, might his distribution of expected AFL player birthdays tell you about the distribution of Australian birthdays in general?

4. Would/should this data discourage a young Australian-born person from aiming for an AFL career because he has a late-year birthday? What are some other considerations in making such a career decision?

Submitted by Margaret Cibes