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Suffolk University poll in Nevada: Clinton with marginal advantage


Suffolk University published the results of a new poll on October 10. In this poll, respondents from Nevada were asked for whom they will vote: Democrat candidate Hillary Clinton or Republican candidate Donald Trump.

Nevada is traditionally a swing state, where the candidates of both major parties have often won similar voter support. This is why the election outcome in that state is viewed as important in determining which party will win the majority of electoral votes.

Suffolk University poll results




According to the results, 44.0% of participants are going to give their vote to former New York Senator Hillary Clinton, while 38.0% would vote for businessman Donald Trump.

The poll was conducted from September 27 to September 29 with 500 likely voters. Considering the poll's margin of error of +/-4.4 percentage points, the results reflect a statistical tie.

Putting the results in context

As a general rule, however, one should not have too much faith in the results of single polls, because they sometimes incorporate large errors. Rather, one should check how a poll's results compare to benchmark forecasts.

For the following analysis, we convert the candidates' raw poll numbers into shares of the two-party vote. This procedure yields values of 53.7% for Clinton and 46.3% for Trump.

Results compared to other polls

If we look at an average of Nevada polls, Clinton's current two-party vote share is at 50.7%. In comparison to her numbers in the Suffolk University poll Clinton's poll average is 3 percentage points lower. This difference is within the poll's error margin, which suggests that the poll is not an outlier.

Comparison to the combined PollyVote

The current PollyVote foresees Clinton to gain 51.1% of the two-party vote in Nevada. That is, Polly's forecast is 2.6 points below her polling numbers. The PollyVote forecast is therefore 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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