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New Hampshire: New WMUR/UNH poll shows Clinton with 11 points lead

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WMUR/UNH released the results of a new poll, in which respondents from New Hampshire were asked for whom they will vote: Donald Trump or Hillary Clinton.

In New Hampshire, the popular vote is often close. This is the reason why the state is commonly regarded as a purple state, which makes it particularly interesting from a forecasting perspective.

WMUR/UNH poll results
49

Clinton

38

Trump

Of those who answered the question, 49.0% said that they will vote for former New York Senator Hillary Clinton, while 38.0% revealed that they would give their vote to businessman Donald Trump.

The poll was conducted between November 3 and November 6. The sample size was 707 likely voters. There is a sampling error of +/-3.7 percentage points. Considering this error margin, the gap between both candidates is statistically significant.

Putting the results in context

As a general rule, however, a good strategy is to not have too much faith in the results of single polls, since they often contain large errors. Rather, one should examine how a poll's results compare to benchmark forecasts.

To make the results comparable to forecasts from other methods, you can translate them into two-party vote shares. This procedure results in values of 56.3% for Clinton and 43.7% for Trump. In the most recent WMUR/UNH poll on October 30 Clinton obtained only 54.1%, while Trump obtained 45.9%.

Results compared to other polls

Clinton is currently at 50.2% of the two-party vote according to an average of recent polls in New Hampshire. This value is 6.2 percentage points lower than her corresponding numbers in the WMUR/UNH poll. This deviation is outside the poll's margin of error, which means that the poll is an outlier.

Comparison to the combined PollyVote

The current PollyVote anticipates Clinton to gain 53.0% of the two-party vote in New Hampshire. Hence, the PollyVote forecast is 3.3 points below her polling numbers. The PollyVote forecast is thus within 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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