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Pennsylvania: 5 points lead for Clinton in new Quinnipiac poll


Results of a new poll administered by Quinnipiac were spread. The poll asked respondents from Pennsylvania for whom they will vote: Republican nominee Donald Trump or Democrat nominee Hillary Clinton.

Quinnipiac poll results




Of those who replied, 48.0% said that they are going to vote for former New York Senator Hillary Clinton, while 43.0% said that they would give their vote to real estate developer Donald Trump.

The poll was carried out from September 27 to October 2 with 535 likely voters. There is a sampling error of +/-4.2 percentage points. Considering this error margin, the race is currently a statistical tie.

Putting the results in context

In general, however, one should not have too much faith in the results of single polls, as they sometimes incorporate large errors. Instead of relying on results from single polls, research in forecasting recommends to rely on combined polls or, even better, the combined PollyVote forecast that incorporates different methods and data.

To make the results comparable to benchmark forecasts, one can translate them into two-party vote shares. This procedure results in values of 52.8% for Clinton and 47.3% for Trump. In the latest Quinnipiac poll on August 7 Clinton received only 44.7%, while Trump received only 0.0%.

Results vs. other polls

If we look at an average of Pennsylvania polls, Clinton's current two-party vote share is at 55.4%. Relative to her numbers in the Quinnipiac poll Clinton's poll average is 2.6 percentage points higher. This margin is within the poll's margin of error, which suggests that the poll is not an outlier.

Comparison to the combined PollyVote

The latest PollyVote predicts Clinton to gain 53.6% of the two-party vote in Pennsylvania. That is, Polly's combined forecast is 0.8 points above her polling numbers. The PollyVote forecast is thus within the poll's error margin.

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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