Biology Magazine

Prediction Requires Data

Posted on the 08 November 2012 by Ccc1685 @ccc1685

Nate Silver has been hailed in the media as a vindicated genius for correctly predicting the election. He was savagely attacked before the election for predicting that Obama would win handily. Kudos also go to Sam Wang,,, and all others who simply took the data obtained from the polls seriously. Hence, the real credit should go to all of the polling organizations for collectively not being statistically biased.  It didn’t matter if single organizations were biased one way or the other as long as they were not correlated in their biases. The true power of prediction in this election was that the errors of the various pollsters were independently distributed. However, even if you didn’t take the data at face value, you could still reasonably predict the election. Obama had an inherent advantage because he had more paths to winning 270 electoral votes. Suppose there were 8 battleground states and Romney needed to win at least 6 of them. Hence, Romney had 28 ways to win while Obama had 228 ways to win. If the win probability was approximately a half in each of these states, which is what a lot of people claimed,  then Romney has slightly more than one in ten chance of winning, which is close to the odds given by Sam. The only way Romney’s odds would increase is if the state results were correlated in his favour. However, it would take a lot of correlated bias to predict that Romney was a favourite.

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