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New Mason-Dixon poll in Florida: Clinton and Trump virtually tied

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Mason-Dixon released the results of a new poll, in which respondents from Florida were asked for whom they will vote: Democrat Hillary Clinton or Republican Donald Trump.

In Florida, the election outcome is often decided by a narrow margin. This is why the state is commonly regarded as a battleground state, which makes it particularly interesting from a forecasting perspective.

Mason-Dixon poll results
44

Clinton

42

Trump

Of those who responded, 44.0% said that they intend to vote for former New York Senator Hillary Clinton, whereas 42.0% said that they would give their vote to billionaire Donald Trump.

The poll was conducted between August 22 and August 24. The sample size was 625 likely voters. There is a sampling error of +/-4.0 percentage points. Considering this error margin, the race is currently a statistical tie.

Putting the results in context

As any other method, polls are subject to bias. Therefore, as a general rule, don't have too much confidence in the results of an individual poll. Rather, one should check how a poll's results compare to benchmark forecasts.

For the following analysis, we translate Clinton's and Trump's raw poll numbers into two-party vote shares. This procedure yields values of 51.2% for Clinton and 48.8% for Trump.

Comparison to other polls

An average of recent polls in Florida has Clinton at 51.2% of the two-party vote. This value is 0 percentage points higher than her respective numbers in the Mason-Dixon poll. This deviation is within the poll's sampling error, which suggests that the poll is not an outlier.

The poll in comparison with PollyVote's forecast

The most recent PollyVote forecasts Clinton to gain 51.5% of the two-party vote in Florida. This means that the combined PollyVote is 0.3 points above her polling numbers. The PollyVote forecast is therefore 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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