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Pennsylvania: Narrow advantage for Clinton in new PPP (D)PPP (D) poll

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Results of a new poll conducted by PPP (D)PPP (D) were released. The poll asked respondents from Pennsylvania for whom they will vote: Hillary Clinton or Donald Trump.

PPP (D)PPP (D) poll results
49

Clinton

45

Trump

The results show that 49.0% of participants would vote for former First Lady Hillary Clinton, while 45.0% would vote for billionaire Donald Trump.

The poll was carried out from July 29 to July 31 among 1505 likely voters. There is a sampling error of +/-2.5 percentage points. Considering this error margin, the race is currently 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 sometimes contain large errors. Instead of relying on results from single polls, forecasting research recommends to look at combined polls or, even better, a combined forecast that draws upon forecasts from different methods, each of which draws upon different data.

For the following comparison, we translate Trump's and Clinton's raw poll numbers into shares of the two-party vote. This yields figures of 52.1% for Clinton and 47.9% for Trump. To compare: Only 47.9% was gained by Clinton in the PPP (D)PPP (D) poll on August 7, for Trump this result was only 0.0%.

Comparison to other polls

Looking at an average of Pennsylvania polls, Clinton's two-party vote share is currently at 54.4%. This value is 2.3 percentage points higher than her respective numbers in the PPP (D)PPP (D) poll. This margin is within the poll's margin of error, which means that the poll is not an outlier.

The poll compared with PollyVote's prediction

The most recent PollyVote predicts Clinton to gain 53.2% of the two-party vote in Pennsylvania. Hence, the combined PollyVote is 1.1 points above her polling numbers. The PollyVote forecast is thus in line with the poll's margin of 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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