Miscues in 2016 inform presidential polling data in 2020 – Published 09/16/2020

Miscues in 2016 inform presidential polling data in 2020
By Eric Avidon
TechTarget, Published September 16, 2020

Based on presidential polling data and predictive analytics models, Hillary Clinton was expected to defeat Donald Trump in 2016.


Much of the presidential polling data and predictive modeling, however, failed to get it right and Trump was elected president. Polling organizations didn’t include enough non-college educated voters in their data, and too few polls were run in the days before the election in the states that wound up determining the outcome.

Four years later, pollsters have learned important lessons in order to include the voters they missed in 2016 who ultimately swung the election, and presidential polling data is being collected differently in 2020 than it was before the last presidential election.

But 2020, with a global pandemic hitting the United States harder than any other country and now vast swaths of the West Coast consumed by wildfires, is different also than any year. And whether the changes made to presidential election polling will lead to more accurate predictions this time around won’t be known until the final polls are conducted before the election and the votes are counted.

Matthew Knee, director of analytics at political consulting firm WPA Intelligence, said that while polls got the popular vote correctly, they failed to catch what was happening in the states that swung the election, leading to poor predictive models.

“People focused on national polls, and this is not a national popular-vote election,” he said. “The polls were pretty darn close on the popular vote, but what they missed was Trump pulling off narrow wins in states where there was a lot less polling. And in many of these places Trump really did pull ahead right at the end.”

For example, in Michigan, the Republican National Committee’s model predicted a Trump victory, but that model was done after most polls had been completed, Knee added.

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