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New Star Tribune/Mason-Dixon poll in Minnesota: Clinton holds slim advantage


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

Star Tribune/Mason-Dixon poll results




Of those who replied, 44.0% said that they intend to vote for former First Lady Hillary Clinton, whereas 38.0% declared that they would give their vote to billionaire Donald Trump.

The poll was conducted from September 12 to September 14 among 625 likely voters. If one takes into account the poll's margin of error of +/-4.0 percentage points, the results reflect a statistical tie.

Putting the results in context

In general, however, you should not have too much faith in the results of single polls, because they can incorporate large errors. Rather than trusting the results from single polls, research in forecasting recommends to look at combined polls or, even better, a combined forecast that uses different methods and data.

For the following analysis, we convert Clinton's and Trump's raw poll numbers into two-party vote shares. This procedure yields values of 53.7% for Clinton and 46.3% for Trump. For comparison: 57.8% was obtained by Clinton in the Star Tribune/Mason-Dixon poll on April 27, for Trump this number was only 42.2%.

Results in comparison to other polls

An average of recent polls in Minnesota sees Clinton at 52.4% of the two-party vote. This value is 1.4 percentage points lower than her corresponding numbers in the Star Tribune/Mason-Dixon poll. This margin 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 current PollyVote expects Clinton to gain 54.6% of the two-party vote in Minnesota. That is, Polly's forecast is 0.9 points above her polling numbers. Again, a look at the poll's sampling error shows that this difference is insignificant.

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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