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Iowa: Clinton tied with Trump in new LorasLoras poll


Results of a new poll carried out by LorasLoras were released on October 9. The poll asked respondents from Iowa for whom they will vote: Republican nominee Donald Trump or Democrat nominee Hillary Clinton.

In Iowa, the popular vote is usually decided by a narrow margin. Therefore, the state is commonly considered a battleground state, which makes it particularly interesting from a forecasting perspective.

LorasLoras poll results




The results show that former New York Senator Hillary Clinton and billionaire Donald Trump are on equal standing, each with 42.0% of the vote.

The poll was carried out from September 20 to September 22 among 491 likely voters. If one accounts for the poll's sampling error of +/-4.4 percentage points, the results reflect a statistical tie.

Putting the results in context

Single polls can contain large errors, and should be treated with caution. At the very least, one should check how a poll's results compare to benchmark forecasts.

For the following analysis, we convert Trump's and Clinton's raw poll numbers into shares of the two-party vote. The results of the actual poll mean 50.0 % for Clinton and 50.0 % for Trump concerning the two-party vote share.

Comparison to other polls

Clinton can currently count on 48.1% of the two-party vote in an average of recent polls in Iowa. When compared to the average forecast of other polls Clinton performed 1.9 percentage points better in the poll. This margin is outside the poll's margin of error, which suggests that the poll is an outlier.

Results compared to the combined PollyVote forecast

The most recent PollyVote predicts Clinton to gain 50.5% and Trump 49.5% of the two-party vote in Iowa. Clinton has 0.5 percentage points less when the results of the poll are compared to the combined PollyVote forecast for Iowa. Again, a look at the poll's sampling error indicates that this difference is significant.

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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