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North Carolina: Clinton tied with Trump in new 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: Democrat candidate Hillary Clinton or Republican candidate Donald Trump.

PPP (D) poll results
47

Clinton

47

Trump

The results show that the two candidates can draw on identical levels of support, each with 47.0% of the vote.

The poll was conducted from September 18 to September 20. A total of 1024 likely voters responded. The sampling error is +/-3.1 percentage points. This means that the levels of voter support for both candidates do not differ significantly.

Putting the results in context

As any other method, polls are subject to bias. In general, a good strategy is to not be overly confident the results of an individual poll. At the very least, one should examine how a poll's results compare to benchmark forecasts.

In order to make the results comparable to benchmark forecasts, we convert them into two-party vote shares. The results of the actual poll mean 50.0 % for Clinton and 50.0 % for Trump concerning the two-party vote share. For comparison: 51.2% was gained by Clinton in the PPP (D) poll on March 20, for Trump this result was only 48.8%.

Comparison to other polls

Looking at an average of North Carolina polls, Clinton's current two-party vote share is at 49.3%. In comparison to the average forecast of other polls Clinton performed 0.7 percentage points better in the poll. This difference is outside the poll's sampling error, which suggests that the poll is an outlier.

The poll in comparison with PollyVote's prediction

The most recent PollyVote predicts Clinton to gain 50.0% and Trump 50.0% 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. The PollyVote forecast is thus outside the poll's margin of error.

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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