Bias Correction by Sub-population Weighting for the 2016 United States Presidential Election

Bumjun Park^1,

¹International Department, Hankuk Academy of Foreign Studies, Yongin, South Korea

Cite this paper:
Bumjun Park. Bias Correction by Sub-population Weighting for the 2016 United States Presidential Election. American Journal of Applied Mathematics and Statistics. 2017; 5(3):101-105. doi: 10.12691/ajams-5-3-3

Abstract

The 2016 Presidential Election was an international surprise, as President Donald Trump came back from a seemingly large deficit in the pre-election opinion polls. As most, if not all, of the major polls missed the election results, the public started to doubt the credibility of pre-election polls. This article proposes that there was a methodological error in the polls. The polls used the census data of American population to weigh their data. However, population may not have a correlation with turnout, meaning that a certain population group may not vote much; not contributing to the electorate. For this reason, the polls based on population might systematically over or underestimate a particular candidate. Thereby, the proposition is that the polling agencies should consider the electorate, not the population for modifying the polling results. The proposition is substantiated with a series of statistical simulations supporting the claim that a poll conducted based on the electorate resembles the actual result more accurately. Conclusively, it argues that, as the polls play a pivotal role in affecting the media and the electorate, the improvement of polls is necessary for well-informed forecasts to be available.

Keywords:
election polls bias correction sub-population weighting turnout rate simulation prediction

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