Election Prediction and STEM [Concealment of Bias]

Election Prediction and STEM by Sheldon H. Jacobson.

From the post:

Every U.S. presidential election attracts the world’s attention, and this year’s election will be no exception. The decision between the two major party candidates, Hillary Clinton and Donald Trump, is challenging for a number of voters; this choice is resulting in third-party candidates like Gary Johnson and Jill Stein collectively drawing double-digit support in some polls. Given the plethora of news stories about both Clinton and Trump, November 8 cannot come soon enough for many.

In the Age of Analytics, numerous websites exist to interpret and analyze the stream of data that floods the airwaves and newswires. Seemingly contradictory data challenges even the most seasoned analysts and pundits. Many of these websites also employ political spin and engender subtle or not-so-subtle political biases that, in some cases, color the interpretation of data to the left or right.

Undergraduate computer science students at the University of Illinois at Urbana-Champaign manage Election Analytics, a nonpartisan, easy-to-use website for anyone seeking an unbiased interpretation of polling data. Launched in 2008, the site fills voids in the national election forecasting landscape.

Election Analytics lets people see the current state of the election, free of any partisan biases or political innuendos. The methodologies used by Election Analytics include Bayesian statistics, which estimate the posterior distributions of the true proportion of voters that will vote for each candidate in each state, given both the available polling data and the states’ previous election results. Each poll is weighted based on its age and its size, providing a highly dynamic forecasting mechanism as Election Day approaches. Because winning a state translates into winning all the Electoral College votes for that state (with Nebraska and Maine using Congressional districts to allocate their Electoral College votes), winning by one vote or 100,000 votes results in the same outcome in the Electoral College race. Dynamic programming then uses the posterior probabilities to compile a probability mass function for the Electoral College votes. By design, Election Analytics cuts through the media chatter and focuses purely on data.

If you have ever taken a social science methodologies course then you know:

Election Analytics lets people see the current state of the election, free of any partisan biases or political innuendos.

is as false as anything uttered by any of the candidates seeking nomination and/or the office of the U.S. presidency since January 1, 2016.

It’s an annoying conceit when you realize that every poll is biased, however clean the subsequent number crunching of the numbers may be.

Bias one step removed isn’t the absence of bias, but the concealment of bias.

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