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Nevada: Clinton and Trump virtually tied in new UNLV/Hart Research (D)UNLV/Hart Research (D) poll

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UNLV/Hart Research (D)UNLV/Hart Research (D) published the results of a new poll. In this poll, participants from Nevada were asked for whom they will vote: Republican Donald Trump or Democrat Hillary Clinton.

In Nevada, the election outcome is usually close. Therefore, the state is commonly considered a battleground state, which makes it particularly interesting from a forecasting perspective.

UNLV/Hart Research (D)UNLV/Hart Research (D) poll results
47

Clinton

44

Trump

The results show that 47.0% of respondents intend to vote for former New York Senator Hillary Clinton, whereas 44.0% plan to vote for billionaire Donald Trump.

This poll was conducted from September 27 to October 2, among a random sample of 700 registered voters. The margin of error is +/-3.8 percentage points. This means that the levels of voter support for both candidates do not differ significantly.

Putting the results in context

Single polls should be interpreted with caution, because they may contain large errors. Rather, one should check how a poll's results compare to benchmark forecasts.

For the following analysis, we convert Trump's and Clinton's raw poll numbers into two-party vote shares. This yields figures of 51.7% for Clinton and 48.4% for Trump.

Results in comparison to other polls

Looking at an average of Nevada polls, Clinton's current two-party vote share is at 50.7%. Relative to her numbers in the UNLV/Hart Research (D)UNLV/Hart Research (D) poll Clinton's poll average is 1 percentage point lower. This margin is within the poll's sampling error, which means that the poll is not an outlier.

Results compared to the combined PollyVote prediction

The latest PollyVote anticipates Clinton to gain 51.0% of the two-party vote in Nevada. Hence, the PollyVote is 0.7 points below her polling numbers. The PollyVote forecast is thus within the poll's sampling 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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