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Ipsos/Reuters poll: Trump behind by 7 points

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Ipsos/Reuters released the results of a new national poll, in which respondents were asked for whom they will vote: Hillary Clinton or Donald Trump.

Ipsos/Reuters poll results
44

Clinton

37

Trump

Of those who replied, 44.0% said that they intend to vote for former First Lady Hillary Clinton, while 37.0% revealed that they would give their vote to businessman Donald Trump.

The Internet poll was conducted from September 29 to October 3 with 1239 likely voters. The error margin is +/-2.8 percentage points, which means that the poll results for Trump and Clinton differ significantly.

Putting the results in context

As a general rule, however, you should not have too much faith in the results of single polls, as they sometimes incorporate large errors. Instead of trusting the results from single polls, forecasting research recommends to rely on combined polls or, even better, a combined forecast that incorporates forecasts from different methods, each of which incorporates different data.

To make the results comparable to forecasts from benchmark methods, one can convert them into two-party vote shares. This yields figures of 54.3% for Clinton and 45.7% for Trump. For comparison: Only 53.7% was obtained by Clinton in the Ipsos/Reuters poll on September 26, for Trump this number was 46.3%.

Comparison to other polls

Clinton can currently count on 52.4% of the major two-party vote according to an average of recent polls. This value is 1.9 percentage points lower than respective numbers in the Ipsos/Reuters poll. This difference is within the poll's sampling error, which suggests that the poll is not an outlier.

The poll compared with PollyVote's prediction

The most recent PollyVote foresees Clinton to gain 52.6% of the two-party vote. Hence, Polly's prediction is 1.7 points below 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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