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Latest Emerson poll in North Carolina: Trump trails by a marginal margin

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Results of a new poll carried out by Emerson were spread. The poll asked respondents from North Carolina for whom they will vote: Democrat candidate Hillary Clinton or Republican candidate Donald Trump.

Emerson poll results
46

Clinton

42

Trump

Of those who replied, 46.0% said that they will vote for former Secretary of State Hillary Clinton, whereas 42.0% said that they would give their vote to billionaire Donald Trump.

This poll was conducted from October 10 to October 12, among a random sample of 600 likely voters. The error margin is +/-3.9 points, which means that the levels of voter support for both parties' candidates do not differ significantly.

Putting the results in context

Single polls should be treated with caution, as they can incorporate substantial biases. Instead of trusting the results from single polls, research in forecasting recommends to rely on combined polls or, even better, the combined PollyVote forecast that relies on forecasts from different methods, each of which draws upon different data.

In order to make the results comparable to forecasts from benchmark methods, we translate them into shares of the two-party vote. This procedure results in figures of 52.3% for Clinton and 47.7% for Trump.

Comparison to other polls

Looking at an average of North Carolina polls, Clinton's current two-party vote share is at 51.8%. Relative to her numbers in the Emerson poll Clinton's poll average is 0.5 percentage points lower. This difference is within the poll's error margin, which suggests that the poll is not an outlier.

Results compared to the combined PollyVote prediction

The most recent PollyVote forecasts Clinton to gain 50.3% of the two-party vote in North Carolina. That is, Polly's combined forecast is 2.0 points below her polling numbers. The PollyVote forecast is therefore within 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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