Poll Dancing 4


Poll Dancing - The Out Of My Mind BlogA neighbor stopped me on the street the other day to share a moment of panic.

“If the presidential election were held yesterday, Donald Trump would have won,” she said, citing a report on the previous evening’s 11 o’clock news.

“You know,” I replied, “why don’t we take a poll on election day instead of voting? We’d inconvenience a thousand or so people with early morning phone calls, but the rest of us could go about our business undisturbed.”

I can never get enough of the deer-in-the-headlights expression that always accompanies this suggestion.

(If, perchance, you think this is a good idea, stop watching Fox News immediately.)

Every one of those polls breaks the Law of Large Numbers. The only question is to what degree.

It was an Italian mathematician named Luca Pacioli who fathered the  agony and the ecstasy that we know as public opinion polling.

The year was 1494 and Pacioli, perhaps looking for some excitement after the whole Columbus-discovering-America affair, got in over his head trying to solve this problem:

Two players engage in a best 3-out-of-5 dice-rolling contest. After three rounds, the first player is up 2 to 1, but the game is called off. How, Pacioli wondered, should the prize be distributed?

Nobody officially won, but certainly the first player had a better chance of winning than the second, if the game had progressed. And, that was the glitch.

Pacioli was looking to mathematics to predict an event that hadn’t happened yet. But mathematicians of the day, who firmly followed Aristotle’s teachings, believed math could not do that. They wanted no part of Pacioli’s quixotic quest.

Pacioli died and the problem lingered, unsolved, until 1654 when Pierre de Fermat, a French lawyer and mathematician, proved that three-quarters of the prize should go to player one and one-quarter to player two. The proof was intuitively simple, and eventually dislodged the inertia of thousands of generations of mathematical Luddites.

Within a few decades mathematicians developed statistics, probability, the bell curve, statistical inference, and actuarial tables. But it was Jacob Bernoulli, a 17th-century Swiss mathematician, who laid down the law behind modern polling.

The Law of Large Numbers.

In conversational terms, the law says that if you have a huge barrel of jelly beans, some of which are red and the rest blue, you can estimate the percentage of red and blue jelly beans in the barrel by counting the red beans in a random sample.

Bernoulli’s math worked flawlessly for either/or events, such as a jelly bean being either red or not red, but another 200 years would pass before Russian mathematician Pafnuty Chebyshev extended Bernoulli’s work to situations in which the barrel contained jelly beans with any number of arbitrary colors.

If you think of the colors as being opinions, such as which presidential candidate a jelly bean is likely to vote for, it’s Chebyshev’s work that turned the Law of Large Numbers into justification for public opinion measurement. Nevertheless, Bernoulli’s original work remains as the heartbeat of those news-making polls.

I suspect television covers opinion polls relentlessly because of the medium’s fascination with law breakers. And every one of those polls breaks the Law of Large Numbers. The only question is to what degree.

Bernoulli would be the first to admit that Aristotle was partially correct. Math doesn’t predict the future with certainty. At best it offers a guesstimate of future results. How good that guesstimate is depends on how random your samples are.

And when it comes to random samples, it’s easier to break the law than follow it.

The instant you decide how to include people in your sample, the less random your sample becomes.

Call voters on a landline telephone? You’re excluding those who don’t have landlnes, or voters who don’t answer calls from strangers during dinner.

Go door to door?

Voters who work tend not to be home during the day. Upscale voters may spend their evenings at the opera or a popular eatery.

Stop voters on the street?

In Los Angeles you’ll get a colorful, albeit highly skewed, sample of Americans if you stick to people who are walking.

Since the best we can do is construct what’s called quasi-random samples—samples that are close enough to being random that they’d almost satisfy Bernoulli—television ought to report what would be a more telling measurement, in this case the odds that Trump will win, not merely the guess that he will.

And if Channel 4 would tell my neighbor not to worry, that the election wasn’t held yesterday, that wouldn’t be a bad idea, either.

 

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Jacob Bernoulli - The Out Of My Mind Blog

Courtesy of Wikimedia Commons

Jacob Bernoulli was born in Basel, Switzerland. While his father mandated that his son was to study theology, Bernoulli included mathematics and astronomy in his education. From 1676 to 1682 Bernoulli traveled throughout Europe, learning about the latest discoveries in mathematics and the sciences from the era’s leading practitioners. Shortly after returning to Switzerland, he began teaching mechanics at the University in Basel, and he settled into a comfortable, and productive, research career. His travels allowed him to maintain relationships with his contemporaries, many of whom were leading mathematicians and scientists. He was appointed professor of mathematics at the University of Basel in 1687, and remained in this position until his death in 1705.

 

Mind Doodle…

Gather 23 randomly selected people in a room and you have a 50/50 chance two of them will have the same birth date (month and day). But what happens if two of those people are twins?

Illustration: Maialisa/Pixabay (Rights: Public Domain)

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4 thoughts on “Poll Dancing

  • Nick Iuppa

    Great piece, but did you really explain the law of large numbers, that everyone seems to be breaking? Don’t think so, but I never was any good at math, so maybe you did.

    • Jay Douglas Post author

      Hi Nick…

      Good question. I tried to simplify it, but maybe I went too far. Here’s a more detailed explanation. If you never liked math you might want to drink a few cups of coffee first.

      Let’s say I have an old jar of coins in my closet. Over the years, I’ve saved 10,000 pennies, nickels, dimes, and quarters. I’ve thrown coins in the jar randomly every day…whatever change I had in my pocket. Now, if I count each type of coin I can know, with 100% certainty, what percentage of each coin I have.

      But I don’t want to do that. Thanks to the Law of Large Numbers, I don’t have to. It provides the mathematical justification for projecting a measurement through the use of random samples.

      The Law of Large Numbers (as extended by Chebyshov) says that if I scoop out a lot of random samples, and compute the percentages of pennies, nickels, dimes, and quarters in each sample, the results will eventually converge on the actual percentages in the whole jar.

      But converge doesn’t mean equal. I won’t be able to tell with certainty whether I’m right or not. Fortunately, the Law of Large Numbers lets me see how good my results are. It’s this “how good” part that doesn’t show up on the TV news.

      For each sample, I can compute a value (let’s say 85% pennies), a margin of error (plus or minus 2%) and a level of confidence (95%).

      Taking all these numbers together, I know that there are most likely 8500 pennies in the jar, although the actual value is somewhere between 8300 and 8700 pennies, And, if I take 100 samples I’ll get these same result 95 times (out of 100).

      If I want better results—say plus or minus 1% with a level of confidence of 98%—I have to take more and larger samples. And, each sample has to be random. I can’t cheat by reaching in the jar and letting smaller coins slip through my fingers (to make it appear I have a higher percentage of quarters).

      Without the mathematical proof the Law of Large Numbers provides, there would be no reason to assume that measuring samples would do anything more than occupy my time for a while.

      I hopes this helps. But, what’s really important to know is that polling can never provide exact answers, even though that’s the way poll results are presented. And, unless you know the conditions under which the poll was performed, you don’t even know how much you can trust the results.

      — jay

    • Jay Douglas Post author

      Hi Jeff…

      Thanks for the comment. I was afraid the word “mathematics” would scare everyone away.

      Sadly, the problem with statistics, and their interpretation, goes far beyond polling. “The average worker makes…,” “Productivity has declined…,” and similar pronouncements based on statistics are useless without supporting information (average can refer to the mean or median, decline implies there’s a base for comparison). Figuring out what the numbers mean without that data is little more than an exercise in self-delusion.

      — jay.