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MIRS-GSCI/Target Insyght*MIRS-GSCI* poll in Michigan: Clinton with 5 points lead

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Results of a new poll carried out by MIRS-GSCI/Target InsyghtMIRS-GSCI were spread. The poll asked participants from Michigan for whom they will vote: Republican Donald Trump or Democrat Hillary Clinton.

MIRS-GSCI/Target InsyghtMIRS-GSCI poll results
46

Clinton

41

Trump

Of those who replied, 46.0% said that they will vote for former Secretary of State Hillary Clinton, while 41.0% said that they would give their vote to businessman Donald Trump.

The poll was carried out from September 18 to September 24 among 600 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

Individual polls often include substantial errors, which is why they should be treated with caution. Instead of relying on results from single polls, research in forecasting recommends to rely on combined polls or, even better, the combined PollyVote forecast that uses forecasts from different methods, each of which draws upon different data.

To make the results comparable to forecasts from benchmark methods, one can convert them into two-party vote shares. This procedure results in values of 52.9% for Clinton and 47.1% for Trump.

Comparison to other polls

If we look at an average of Michigan polls, Clinton's two-party vote share is currently at 54.0%. This value is 1.1 percentage points higher than her corresponding numbers in the MIRS-GSCI/Target InsyghtMIRS-GSCI poll. This deviation is within the poll's error margin, which means that the poll is not an outlier.

Results compared to the combined PollyVote forecast

The most recent PollyVote predicts Clinton to gain 53.6% of the two-party vote in Michigan. Hence, the PollyVote is 0.7 points above her polling numbers. The PollyVote forecast is thus in line with the poll's error margin.

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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