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Pennsylvania: Clinton holds strong advantage in Franklin & Marshall poll

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Franklin & Marshall published the results of a new poll. In this poll, interviewees from Pennsylvania were asked for whom they will vote: Democrat candidate Hillary Clinton or Republican candidate Donald Trump.

Franklin & Marshall poll results
49

Clinton

38

Trump

Of those who answered the question, 49.0% said that they are going to vote for former Secretary of State Hillary Clinton, while 38.0% declared that they would give their vote to billionaire Donald Trump.

The poll was conducted from July 29 to August 2. A total of 389 likely voters responded. Taking into account the poll's margin of error of +/-6.3 percentage points, the results reflect a statistical tie.

Putting the results in context

As a general rule, however, don't have too much faith in the results of single polls, because they can contain large errors. Rather than relying on results from single polls, the evidence-based approach is to look at combined polls or, even better, the combined PollyVote forecast that draws upon different methods and data.

To make the results comparable to forecasts from benchmark methods, you can convert them into two-party vote shares. This procedure results in values of 56.3% for Clinton and 43.7% for Trump.

Results in comparison to other polls

Looking at an average of Pennsylvania polls, Clinton's two-party vote share is currently at 55.1%. Relative to her numbers in the Franklin & Marshall poll Clinton's poll average is 1.2 percentage points worse. This difference is within the poll's error margin, which means that the poll is not an outlier.

Results compared to the combined PollyVote forecast

The current PollyVote anticipates Clinton to gain 53.4% of the two-party vote in Pennsylvania. That is, Polly's combined forecast is 2.9 points below her polling numbers. The PollyVote forecast is therefore in line with 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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