Days before the 2016 presidential election that vaulted Donald Trump to the White House, The New York Times announced that Hillary Clinton had a 91% chance of winning. The Times wasn’t alone in its prediction. Virtually every model reached the same wrong conclusion.
The results eroded the public’s trust in polls and in the pundits who rely on them for talking points. It’s not surprising that people are questioning the validity of current polls that show Democratic presidential candidate Joe Biden ahead of Trump, even in the electoral map.
“There’s a lot of market pressure to try to get [polls] to be as accurate as possible. Everybody has been working as hard as they can,” Wharton statistics professor Abraham (Adi) Wyner says about the upcoming election. Polls aren’t perfect, he noted, but they are increasingly valuable in a world obsessed with data science and predictive analytics. Wyner, who also serves as a faculty lead of the Wharton Sports Analytics and Business Initiative, explains how poll data is collected and analyzed.
According to Wyner, there are three reasons why polls can be so problematic. One, it’s not an exact science. Despite all the number crunching, polls don’t always call the outcome correctly because there are so many variables involved in an election. Second, it depends on who is polled. Demographics play a key role in election surveys, which means something as seemingly innocuous as the method of contact can change the results. And third, voters can—and do—change their minds. The current political climate is so polarized that it seems reasonable to assume there are no undecided voters in the upcoming election.
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