We have now witnessed the election of Donald Trump to be the next President, and the Democratic party has had immeasurable amounts of self-inflicting anguish and scrutiny from all sections of registered citizens. Everyone from the urban dwellers to the highest classes of society have been expressing shock and fury over something that could not have been imagined. Many voters have turned to justifiably accusing the fractured, desolate contemporary American life: urban from suburb, white from black, bourgeoisie from proletariat, Republican from Democrat, conformist to independent, and so on. Not a single member of the democratic republic of the United States should remain at ease over the utter lack of communication and unification across the gaping crevasses dividing our policy from county to county.

So what exactly went wrong?

There are obviously a multitude of reasons why the majority of America was stunned by this election; however, the most prominent one remains the overwhelming consensus among statisticians that Hillary Clinton was going to win the race. Nate Silver, a well-known and heavily trusted political researcher, predicted Hillary Clinton's odds of winning the election at 85% on November 8th based on the poll statistics he had gathered. This approach clearly did not lead him in the right direction. We now have an important question to ask ourselves; just how reliable are poll statistics? The preferred method used to predict this election and dozens of elections before it were these same polls taken by millions of different people in every possible part of the country, but this election had a twist to it.

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The stigma surrounding the endorsement of either Donald Trump or Hillary Clinton in some regions was so influential that a majority of poll participants were likely not being truthful about their opinions and their beliefs. If the results of these polls cannot accurately account for this misrepresentation, then what is the benefit to using them as statistics? The trouble with relying on the results of preliminary polling is that polling has no protection system against the human factor of preliminary dishonesty.

Is this the end of science in politics?

The short answer to this question is no, because the integration of mathematical and statistical approaches to political representation is just as fundamental as the human approach. It is no secret that even the most devoted political science pundits that say polling is dead have simply unified their preferred arguments into an explanation for what just happened. Don't get me wrong, what just happened may be one of the most significant events in the recent history of this country; but to say that elections from this point on will flip over on themselves completely due to somewhat unexpected precedents is nothing more than a blatant hyperbole.

Now, there will have to be renovations to the current system of civic forecasting in order for it to be more accurate; ignoring the humanity of voting with a densely piled stack of numbers is no longer a viable option for politically inclined statisticians. So, in other words, if experts had paid closer attention to the political part of political science, they would have done better. Hopefully this is just another lesson to learn, but that will be up to the pundits in charge of learning it.

Fair targets

Nate Silver pointed out in a tweet hours after Trump won that the gap between popular and electoral results was the largest since 1948. This gap undoubtedly deserves further analysis; this is the second time in the past five presidential election cycles that the popular vote and the electoral college don’t match up. The problem is that officials are actually angry at the prediction sites such as the Princeton Election Consortium that radically underestimated Trump’s chances of victory. Given the outcome, those are fair targets. But they’re not political science targets.