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Suffolk University poll in Pennsylvania: Trump trails by 9 points

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Suffolk University released the results of a new poll, in which respondents from Pennsylvania were asked for whom they will vote: Hillary Clinton or Donald Trump.

Suffolk University poll results
50

Clinton

41

Trump

Of those who responded, 50.0% said that they are going to vote for former New York Senator Hillary Clinton, whereas 41.0% revealed that they would give their vote to businessman Donald Trump.

The poll was in the field between July 25 and July 27. The sample size was 500 likely voters. If one takes into account the poll's margin of error of +/-4.4 percentage points, the spread in voter support is statistically significant.

Putting the results in context

As any other method, polls are subject to bias. Therefore, as a general rule, you should not be overly confident the results of an individual poll. Instead of trusting the results from single polls, forecasting research recommends to rely on combined polls or, even better, the combined PollyVote forecast that includes forecasts from different methods, each of which includes different data.

To make the results comparable to benchmark forecasts, you can convert them into shares of the two-party vote. This yields figures of 55.0% for Clinton and 45.1% for Trump.

Results vs. other polls

Clinton can currently count on 55.2% of the two-party vote according to an average of recent polls in Pennsylvania. This value is 0.2 percentage points higher than her corresponding numbers in the Suffolk University poll. This margin is within the poll's sampling error, which suggests that the poll is not an outlier.

The poll compared with PollyVote's prediction

The most recent PollyVote foresees Clinton to gain 53.3% of the two-party vote in Pennsylvania. This means that the combined PollyVote is 1.7 points below her polling numbers. Again, a look at the poll's error margin shows that this difference is insignificant.

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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