Race and sex were determined by interviewers in surveys before 1984. In 2012, 20, the exit poll was conducted by Edison/Mitofsky in 19 by Voter News Services in 1992 by Voter Research and Surveys and in earlier years by The New York Times and CBS News.ĭirect comparisons from year to year should factor in differences in how questions were asked. The voter survey is based on questionnaires completed by 24,537 voters leaving 350 voting places throughout the United States on Election Day including 4,398 telephone interviews with early and absentee voters. This feedback is private to you and won’t be shared publicly.Data for 2016 were collected by Edison Research for the National Election Pool, a consortium of ABC News, The Associated Press, CBSNews, CNN, Fox News and NBC News. Mark contributions as unhelpful if you find them irrelevant or not valuable to the article. To conclude, data analytics is certainly not dead. Data analytics has become an integral part of almost every major organization and continues to transform the world in ways previously thought impossible. They need to step back and think about the challenges of collecting data, frequent shift in votes and the extremely small error in prediction models which led to such different forecasts. In the end, people are blaming the polls and the analytics behind it a little too much. After all, the blame of killing someone with a knife rests with the person wielding it and not with the knife. Moreover, they should not blame human error and incorrect application of analytics techniques on the entire field of data analytics. These polls were able to make correct predictions because they used novel data collection techniques and were successfully able to track and factor in the different voter groups.ĭetractors who view the 2016 elections as proof of death of data forget that data scientists such as Nate Silver, who analyses elections and baseball and also developed the ‘ELO’ rating system for Major League Baseball have been able to correctly predict the election outcomes for a number of years now. Times and Investor’s Business Daily predicted a win for Donald Trump. Most traditional ‘likely-voter’ models include measures of enthusiasm, 2016’s distinctly unenthused electorate may have also wreaked some havoc with this aspect of measurement. The voters that polltakers were expecting, especially in the Midwestern and Rust Belt states that so defied expectations, were not the ones that showed up. This is an extremely difficult task, and minor differences in assumptions can lead to vastly varying predictions. Since it is not possible to know in advance who is actually going to vote, polltakers develop prediction models to estimate that who is going to vote and what the electorate will look like on Election Day. It is also important to look at the method pollsters use to identify likely voters. This notion of the so-called “shy Trumpers” indicates that support for Trump was socially undesirable, and that his supporters were not willing to admit their support to pollsters. Another reason could have been that many of those who were polled were not honest about whom they intended to vote for. This would have resulted in a strong pro-Trump segment of the population that did not show up in the polls in proportion to their actual share of the population. The frustration and anti-institutional feelings that drove the Trump campaign may also have aligned with an unwillingness to respond to polls. It occurs when certain sections or groups do not respond to surveys even though they were presented with an equal opportunity. The small number of votes required by Clinton to completely turn the results in her favour is well within the acceptable margin of error. This error in the models could have been introduced due to “nonresponse bias”.
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