Quote:
Originally Posted by supernothing  Can you explain please? |
There are only 116 numbers. The stats are testing the entire population of results not a sample, there are no larger numbers to be tested.
So in effect it's the population that the statistics are being run on and not a sample. If you hold that the basic contention (as follows) for doing this is correct:-
Quote:
We'll concentrate on vote counts -- the number of votes received by different candidates in different provinces -- and in particular the last and second-to-last digits of these numbers. For example, if a candidate received 14,579 votes in a province (Mr. Karroubi's actual vote count in Isfahan), we'll focus on digits 7 and 9.
This may seem strange, because these digits usually don't change who wins. In fact, last digits in a fair election don't tell us anything about the candidates, the make-up of the electorate or the context of the election. They are random noise in the sense that a fair vote count is as likely to end in 1 as it is to end in 2, 3, 4, or any other numeral. But that's exactly why they can serve as a litmus test for election fraud. For example, an election in which a majority of provincial vote counts ended in 5 would surely raise red flags.
Why would fraudulent numbers look any different? The reason is that humans are bad at making up numbers. Cognitive psychologists have found that study participants in lab experiments asked to write sequences of random digits will tend to select some digits more frequently than others.
|
Essentially you would expect a true election to show a more random distribution of numbers. Here there is a statistically significant result that suggests the numbers are not significantly different from one another.
