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North Carolina: Clinton tied with Trump in latest PPP (D) poll

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PPP (D) published the results of a new poll. In this poll, participants from North Carolina were asked for whom they will vote: Hillary Clinton or Donald Trump.

PPP (D) poll results
47

Clinton

47

Trump

According to the results, real estate developer Donald Trump and former New York Senator Hillary Clinton can draw on the same level of support, each with 47.0% of the vote.

The poll was conducted from September 18 to September 20 among 1024 likely voters. There is a sampling error of +/-3.1 percentage points. Considering this error margin, the race is currently a statistical tie.

Putting the results in context

As any other method, polls are subject to bias. Therefore, as a general rule, you should not be too confident the results of an individual poll. At least, one should check how a poll's results compare to benchmark forecasts.

For the following comparison, we translate the candidates' raw poll numbers into shares of the two-party vote. The results of the actual poll mean 50.0 % for Clinton and 50.0 % for Trump in the two-party vote share. In the latest PPP (D) poll on March 20 Clinton received 51.2%, while Trump received only 48.8%.

Comparison to other polls

An average of recent polls in North Carolina has Clinton at 49.3% of the two-party vote. When compared to the average forecast of other polls Clinton performed 0.7 percentage points better in the poll. This deviation is outside the poll's margin of error, which suggests that the poll is an outlier.

Results compared to the combined PollyVote prediction

The most recent PollyVote predicts Clinton to gain 50.0% and Trump 50.1% of the two-party vote in North Carolina. Clinton has 0 percentage points more when the results of the poll are compared to the combined PollyVote forecast for North Carolina. Again, a look at the poll's margin of error shows that this difference is significant.

This article was automatically generated by the PollyBot, which uses algorithms developed by AX Semantics to generate text from data stored in our API. The exact dataset underlying this particular article can be found here.

Please let us know if you find any typos, missing words, or grammatical errors. Your feedback helps us to further improve the quality of the texts.

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