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November 07, 2006



Given a set of poll results, the question is whether with all that data there is some other concise figure as relevant as the mean. After reading your article, it occured to me that fitting to a bell curve, which has only two cumulants (mean and standard deviation), provides no other information at all.

To get more information, you'd need a fit that includes the third cumulant, skewness. The curve would have a peak that is not in the middle, and that peak (the mode) would be another relevant statistic. This could be done by fitting a quartic polynomial to the logarithm of the Fourier Cosine Transform of the data.

I think the phrase "Statistical Tie" could be kept, but with a new meaning: that the mean and the mode of this curve are on opposite sides of 50%.


The curve would have a peak that is not in the middle, and that peak (the mode) would be another relevant statistic.


It's not really accurate to say that they "fit" polling data to a bell curve. The bell curve is used only to determine the sampling error — the only error information actually reported in these polls. In fact, that's not even totally accurate. There is a formula for determining the sampling error that was derived based on sample error being distributed along a bell curve.

The sampling error is the error caused by polling a subset of voters. So the sampling error they report is really the answer to the question "how far off could the poll be merely because we might have accidentally not polled a representative population?" (Actually, the question needs to add the additional information that we want to know how far off we could be such that 95% of the time we'd be within that range.) The sampling error is determined only by plugging the number of voters polled and the estimated total number of voters into a standard formula.


I agree completely..


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