The Quick Count and Election Observation

THE QUICK COUNT AND ELECTION OBSERVATION
ples are followed, then accurate estimates of the distribution of the vote for
the entire country can be made on the basis of a properly drawn sample. It is
possible to make very accurate estimates about the behavior of a population
(how the population voted) on the basis of a sample (of the results at selected
polling stations) because of the theory of probability.
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Probability: The Law of Large Numbers and the Central Limit Theorem
Probability concerns the chance that an event, or an outcome, will occur. It is
possible to estimate the probability of unknown future events – that Brazil will
win the World Cup, or that it will rain today. No one knows ahead of time what
will happen, but it is possible to make an educated guess based on the team’s
performance in other events, or the meteorological conditions outside. It is
also possible to make predictions about probability based on the known likelihood
that something will happen. Consider the classic statistical example of
tossing a fair coin, one that is unbiased:
A coin is tossed in the air 100 times. With a fair coin, the chances are
that the outcome will be heads 50 times and tails 50 times, or something
very close to that. Suppose now that the same rule was tested
using only a few tosses of the same coin. Tossing that same coin 12
times in the air might produce outcomes that are not exactly even.
The outcome could be 9 heads and 3 tails. Indeed, in exceptional circumstances,
it is possible that, with twelve throws, the coin could land
heads up every time. In fact, the probability that such an unusual outcome
will occur can be calculated quite precisely. The probability of
twelve heads in a row turns out to be one in two to the twelfth power
(1/2) 12 , or one in 4,096 or 0.024 percent. That is, the chance of getting
twelve heads (or tails) in a row is one in four thousand and ninety
six. Probability theory indicates that the distribution between heads
and tails showing will even out in the long run.
One aspect of probability theory at work in the above coin toss example is the
law of large numbers. This statistical principle holds that, the more times that
a fair coin is tossed in the air, the more likely (probable) it is that the overall
distribution of total outcomes (observations) will conform to an entirely predictable
and known pattern. The practical implication is clear: the more data we
have, the more certain we can be about predicting outcomes accurately.
This statistical law of large numbers is firmly grounded in mathematics, but
the non-technical lesson is that there is safety in numbers. A second example
illustrates a related point important to understanding the basis of the quick
count methodology.
Consider a class of 500 students taking the same university course.
Most students will earn Bs and Cs, although a few students will earn
As, and a few will earn Ds, or even F’s. That same distribution of grades
would almost certainly not be replicated precisely if the same course