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Minnesota: Clinton holds substantial advantage in latest KSTP/SurveyUSA poll


Results of a new poll conducted by KSTP/SurveyUSA were distributed on November 6. The poll asked interviewees from Minnesota for whom they will vote: Hillary Clinton or Donald Trump.

KSTP/SurveyUSA poll results




Of those who responded, 53.0% said that they would vote for former New York Senator Hillary Clinton, whereas 42.0% indicated that they would give their vote to billionaire Donald Trump.

The poll was in the field between October 22 and October 25. The sample size was 656 likely voters. Given the poll's margin of error of +/-3.9 percentage points, the spread in voter support is statistically significant.

Putting the results in context

Single polls should be treated with caution, since they may incorporate large errors. Rather than relying on results from single polls, forecasting research recommends to look at combined polls or, even better, the combined PollyVote forecast that includes 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 procedure yields values of 55.8% for Clinton and 44.2% for Trump. In the latest KSTP/SurveyUSA poll on September 20 Clinton received only 53.3%, while Trump received 46.7%.

Comparison to other polls

An average of recent polls in Minnesota has Clinton at 53.6% of the two-party vote. This value is 2.2 percentage points lower than her corresponding numbers in the KSTP/SurveyUSA poll. This deviation is within the poll's sampling error, which suggests that the poll is not an outlier.

Comparison to the combined PollyVote

The current PollyVote expects Clinton to gain 55.4% of the two-party vote in Minnesota. That is, the PollyVote forecast is 0.4 points below her polling numbers. The PollyVote forecast is thus 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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