Predictions of poll outcomes have existed for over six decades, with little or no cases of wrong predictions, as political trends in the United States were pretty similar (Laden, 2015). Wrong predictions began to manifest in 2016 poll predictions, as unlikely candidates are gaining popularity and liking, contrary to the previous forecasts. Leading poll predicting firms led by Nate Silver have expressed their surprise at the turn up of events.
Nate Silver has been predicting poles for a long time, and he is known to have correctly predicted Obama’s win in the previous polls (Jones, 2002). Wrong poles in this year’s campaigns have been blamed on the small sample size of seventeen elections since the Second World War. Therefore, pundits have tried to classify the unexpected polls to foxes and hedgehogs. Foxes are politicians who interfere with the proper flow of views and ideas, like Donald Trump. Donald Trump has opposition even from his party, from the beginning of the presidential campaigns. The ordeal made him have low ratings, and pundits predicted his downfall. However, things played out differently, and Donald Trump is on top of the charts. Hedgehogs, on the other hand, are politicians who thrive to assimilate ideas, and walk with the crowd (Laden, 2015). Such politicians include Ted Cruz and Hillary Clinton, and their polls seem to go uninterrupted with little or no surprises.
Furthermore, political prognostication is a major driver to biases in assumptions. Since 1980, pundits have been making similar assumptions, similar methods, and the same sample size to predict polls and outcomes of elections. Due to the stable economic environment, and demand for improved living standards, elections have been traditionally construed (Jones, 2002). The dynamic economic and political situation worldwide have made polls unpredictable and increased the incidences of biases.
Uncertainty in poll predictions has been attributed to some factors, which pundits have stroke out to be the key drivers. First, economic reformation in the United States is the major drivers. Poll biases, as many people have called the wavering poll predictions, occur in developed states where citizens have adopted the postmodern way of life. About fifty percent of the American population lives in the few states, presumably 146 states. The rest of the population lives in marginalized states where poll turnouts and predictions are unwavering. Apparently, people living in postmodern states judge the success of presidential candidates according to the amount of money their campaigns pump into the economy (Jones, 2002).
Candidates like Donald Trump, who are self-made, and don’t have a lot of campaign donors, gain more favor in the eyes of voters, as more money means a higher likelihood of success. However, political analysts and behaviorists have termed this behavior as unlikely, and argued that once a candidate gains more favor among voters, more money gets pumped his way, and it’s unlikely for more money to earn voters trust.
Lastly, Google searches and the realignment of the political environment has been the primary cause of biases (Laden, 2015). When a political candidate gains popularity in an individual state, search for their related information is likely to hike and does not necessarily translate to increased liking by the public. Therefore, the types of sources used as a basis for prediction in this year’s presidential election campaigns are likely to determine the accuracy of poll predictions.
References
Jones, R. J. (2002). Who will be in the White House?: Predicting presidential elections. New York: Longman.
Laden, G. (November 05, 2015). 2016 US Presidential Election: Trump/Carson/Somebody vs. Clinton (polls). Greg Laden's Blog, 2015-11.
