# Chance News 29

## Contents

## Quotations

"There are few things that are so unpardonably neglected in our country as poker. The upper class knows very little about it. Now and then you find ambassadors who have sort of a general knowledge of the game, but the ignorance of the people is fearful. Why, I have known clergymen, good men, kind-hearted, liberal, sincere, and all that, who did not know the meaning of a "flush." It is enough to make one ashamed of the species".

## Forsooth

The following Forsooths are from the September 07 issue of the RSS NEWS.

Heart disease claimed the lives of one in five men

and about one in six women last year, figures indicate.The Times

26 May 2006

See the end of this Chance News for the data that was the basis for this claim.

[Hanson plc is the]] Largest aggregates producer

in the world and 3rd largest in the USADaily Telegraph

3 March, 2006

This Forsooth was suggested by Jerry Grossman.

In addition, a person's odds of becoming obese increased by 57 percent if he or she had a friend who became obese over a certain time interval. If the two people were mutual friends, the odds increased to 171 percent.

This discussion relates to an article The Spread of Obesity in a Large Social Network over 32 Years that appeared in the July 26, 2007 issue of the New England Journal of Medicine and seems to be freely available. Of course, here the "increased to 171 percent" is "increased by 171%."

Jerry remarks "The NEJM article is interesting to those of us interested in the mathematical aspects of the social network."

This forsooth was suggested by Paul Alper

I've done 120 short-term energy outlooks, and I've probably gotten two of them right.

Mark Rodekohr, a veteran Department of Energy (DOE) economist

Minnesota Star Tribune

August 12, 2007

## Second chance lottery drawing

Ask Marilyn

Parade, 5 August 2007

Marilyn vos Savant

A reader poses the following question.

Say that a state runs a lottery with scratch-off tickets and has a second-chance drawing for losing tickets. The latter are sent to a central location, where they are boxed and stored until it’s time for the drawing. An official then chooses one box and draws a ticket from it. All the other boxes are untouched. Is this fair, compared to storing all the tickets in a large container and then drawing a ticket from it?

Marilyn responds that, "The methods are equivalent, and both are perfectly fair: One winner was chosen at random", and suggests that the method is used purely for physical convenience. (In a state lottery, however, we imagine the whole affair would be conducted electronically.)

DISCUSSION QUESTIONS:

(1) Marilyn's answer is almost correct. What has been implicitly assumed here?

(2) Here is a related problem (from Grinstead & Snell, Introduction to Probability, p. 152, problem 23).

You are given two urns and fifty balls. Half of the balls are white and half are black. You are asked to distribute the balls in the urns with no restriction placed on the number of either type in an urn. How should you distribute the balls in the urns to maximize the probability of obtaining a white ball if an urn is chosen at random and a ball drawn out at random? Justify your answer.

Submitted by Bill Peterson

## The understanding and misunderstanding of Bayesian statistics

*Gambling on tomorrow*, The Economist, Aug 16th 2007

*Scientists try new ways to predict climate risks*, Reuters 12 Aug 2007.

*Too late to escape climate disaster?*, New Scientist, 18 Aug 2007.

*Earth Log - Complex lesson*, Daily Telegraph, 17 Aug 2007.

The latest edition of one of the Royal Society's journals, Philosophical Transactions, is devoted to the science of climate modelling:

predictions from different models are pooled to produce estimates of future climate change, together with their associated uncertainties,

the Royal Society said, and it partly focusses on 'the understanding and misunderstanding' of Bayesian statistics. So this Economist article discusses the difference between the frequentist and Bayesian view of statistics, in the context of forecasting the weather.

It starts by claiming that there were just two main influences on the early development of probability theory and statistics:
Bayes and Pascal: Pascal's ideas are simple and widely understood while Bayes are not.
Pascal adopted a frequentist view, which The Economist characterises as *the world was that of the gambler: each throw of the dice is independent of the previous one;*
Bayes promoted what we now call Bayesian probability, which The Economist characterises as *incorporating the accumulation of experience into a statistical model in the form of prior assumptions:*

A good prior assumption about tomorrow's weather, for example, is that it will be similar to today's. Assumptions about the weather the day after tomorrow, though, will be modified by what actually happens tomorrow.

But prior assumptions can influence model outcomes in subtle ways, The Economist warns:

Since the future is uncertain, (weather) forecasts are run thousands of times, with varying parameters, to produce a range of possible outcomes. The outcomes are assumed to cluster around the most probable version of the future. The particular range of values chosen for a parameter is an example of a Bayesian prior assumption, since it may be modified in the light of experience. But the way you pick the individual values to plug into the model can cause trouble. They might, for example, be assumed to be evenly spaced, say 1,2,3,4. But in the example of snow retention, evenly spacing both rate-of-fall and rate-of-residence-in-the-clouds values will give different distributions of results. That is because the second parameter is actually the reciprocal of the first. To make the two match, value for value, you would need, in the second case, to count 1, ½, ⅓, ¼—which is not evenly spaced. If you use evenly spaced values instead, the two models' outcomes will cluster differently.

It goes on to claim that those who use statistical models often fail to account for the uncertainty associated with such models:

Psychologically, people tend to be Bayesian—to the extent of often making false connections. And that risk of false connection is why scientists like Pascal's version of the world. It appears to be objective. But when models are built, it is almost impossible to avoid including Bayesian-style prior assumptions in them. By failing to acknowledge that, model builders risk making serious mistakes.

One of the Philosophical Transactions papers authors', David Stainforth of Oxford University, says

The answer is more comprehensive assessments of uncertainty, if we are to provide better information for today's policy makers. Such assessments would help steer the development of climate models and focus observational campaigns. Together this would improve our ability to inform decision makers in the future.

### Questions

- What influences on the early development of probability theory and statistics can you think of, other than Pascal and Bayes?
- Is the frequentist view of statistics nothing more than
*each throw of the dice is independent of the previous one*. What other characteristics would you associate with this view of statistics? Can you offer a better one-line summary? What about a better descrption of Bayesian statistics than*incorporating the accumulation experience into a statistical model in the form of prior assumptions*. - In one of the Royal Society's papers, authors David Stainforth from Oxford University and Leonard Smith from the LSE, advocate making a clearer distinction between the output of model experiments designed for improving the model and those of immediate relevance for decision making. What do you think they meant by that? Can you think of a simple example to illustrate your interpretation?
- The Economist claims that scientists are not easily able to understand Bayes because of their philosophical training in the rigours of Pascal's method. How would you reply to this assertion?

### Further reading

- Confidence, uncertainty and decision-support relevance in climate predictions, David Stainforth, Oxford University and Leonard Smith, LSE.
- This paper discusses the sources of uncertainty in the interpretation of climate model simulations as projections of the future.

- See also Climateprediction.net.

Sumbitted by John Gavin.

## The Myth, the Math, the Sex

The Myth, the Math, the sex.

*The New York Times*, August 12, 2007, The Week in Review

Gina Kolata

The Median, the Math and the Sex.

*The New York Times*, August 19, 2007, The Week in Review

Gina Kolata

In the first article Gina Kolata comments that there have been numerous studies claiming to show that men have more sexual partners than women.

She reports a recent government study, reporting that men have had a medium of seven female sex partners while women have had a median of four. Kolata writes:

"It is about time for mathematicians to set the record straight," said David Gale, an emeritus mathematics professor at the University of California, Berkeley.

"Surveys and studies to the contrary notwithstanding, the conclusion that men have substantially more sex partners than women is not and cannot be true for purely logical reasons," Dr Gale said. He even provided a proof, writing in an e-mail message.

By way of dramatization, we change the context slightly and will prove what will be called the High School Prom Theorem. We suppose that on the day after the prom, each girl is asked to give the number of boys she danced with. These numbers are then added up giving a number G. The same information is then obtained from the boy, giving a number B

Theorem: G = B

Kolata reports further:

Ronald Graham, a professor of mathematics and computer science at the University of California, San Diego, agreed with Dr. Gale. After all, on average, men would have to have three more partners than women, raising the question of where all these extra partners might be.

The second Gina Kolata article deals primarily with the shower of responses pointing out that the study reported that the medians were different, and so Gale's proof is either irrelevant or not true.

Of course the Blogs had a field day with this mathematics. One of the best is the Blog of Brad Delong, Delong is an economist at the University of California and hence a colleague of David Gale. He blames Gina Kolata saying that she did not tell Gale that the study reported the results of the study in terms of medians rather than means. However the comments to this Blog are very interesting and show just how hard it is to apply mathematics to the real world. Their comments subjext good discussion questions.

### Discussion questions

(1) What explantions can you give for the resutls of the survey? Are they enough to explain the difference reported in this survey.

(2) Did you dance with more than one person at your high school prom?

(3) Is Gales theory true if there are more women than men or more men than women in the population sampled?

(4)The article reports:

I have heard this question before,” said Cheryl D. Fryar, a health statistician at the National Center for Health Statistics and a lead author of the new federal report, “Drug Use and Sexual Behaviors Reported by Adults: United States, 1999-2002,” which found that men had a median of seven partners and women four. But when it comes to an explanation, she added, “I have no idea.”

Do you think that Fryar Knows the difference between mean and median?

## Data for first forsooth

The Times 26 May 2006 article that is the source for the Forsooth included the following data:

LEADING CAUSES OF DEATH

MEN Total deaths Percentage

Heart disease 49,205 20.2

Cerebrovascular diseases 19,266 7.9

Cancer of trachea, bronchus & lung 16,775 6.9

Chronic lower respiratory diseases 13,589 5.6

Influenza and pneumonia 12,209 5

Prostate cancer 9,018 3.7

Cancer of colon, rectum and anus 7,570 3.1

Lymphoid cancer 5,606 2.3

Dementia and Alzheimer's 5,076 2.1

WOMEN Total deaths Percentage

Heart disease 38,969 16

Influenza and pneumonia 31,366 12.9

Dementia and Alzheimer's 19,255 7.9

Chronic lower respiratory diseases 12,605 5.2

Cancer of trachea, bronchus & lung 11,895 4.9

Breast cancer 10,986 4.5

Heart failure & complications, & ill-defined heart disease (not included above) 7,212 3

Cancer of colon, rectum and anus 6,537 2.7