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Pennsylvania: 12 points lead for Clinton in new Franklin & Marshall poll


On October 10, Franklin & Marshall released the results of a new poll, in which respondents from Pennsylvania were asked for whom they will vote: Hillary Clinton or Donald Trump.

Franklin & Marshall poll results




According to the results, 48.0% of respondents said that they would cast a ballot for former New York Senator Hillary Clinton, while 36.0% would vote for real estate developer Donald Trump.

The poll was conducted from September 28 to October 2 among 813 registered voters. The margin of error is +/-4.8 points. This means that the poll results for the candidates of both parties differ significantly.

Putting the results in context

In general, however, one should not have too much faith in the results of single polls, because they can incorporate large errors. Instead of relying on results from single polls, research in forecasting recommends to use combined polls or, even better, the combined PollyVote forecast that incorporates 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. The corresponding figures are 57.1% for Clinton and 42.9% for Trump.

Results compared to other polls

Looking at an average of Pennsylvania polls, Clinton's two-party vote share is currently at 55.2%. Compared to her numbers in the Franklin & Marshall poll Clinton's poll average is 1.9 percentage points worse. This margin is within the poll's error margin, which suggests that the poll is not an outlier.

The poll compared with PollyVote's prediction

The most recent PollyVote forecasts Clinton to gain 53.0% of the two-party vote in Pennsylvania. This means that the PollyVote forecast is 4.1 points below her polling numbers. The PollyVote forecast is thus in line with 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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