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New Gravis poll: Trump and Clinton in a virtual tie


On June 18, Gravis released the results of a new national poll, in which respondents were asked for whom they would vote if the nominees were Hillary Clinton for the Democrats and Donald Trump for the Republican Party.

Of those who answered the question, 51.0% said that they are going to vote for former First Lady Hillary Clinton, while 49.0% declared that they would give their vote to real estate developer Donald Trump.

This poll was conducted from June 16 to June 16, among a random sample of 2197 participants. The margin of error is +/-2.1 percentage points, which means that the levels of voter support for both candidates do not differ significantly.

Putting the results in context

As any other method, polls are subject to bias. Therefore, as a general rule, one should not be too confident the results of a single poll. Rather than trusting the results from single polls, the best practice is to look at aggregated 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 convert Clinton's and Trump's raw poll numbers into shares of the two-party vote. The respective figures are 51.0% for Clinton and 49.0% for Trump. On May 13 Clinton obtained 51.1% in the Gravis poll and Trump obtained only 48.9%.

Comparison to other polls

Looking at the polling average, Clinton's two-party vote share is currently at 53.4%. Compared to her numbers in the Gravis poll Clinton's polling average is 2.4 percentage points higher. This margin is outside the poll's error margin, which suggests that the poll is an outlier.

Comparison to the combined PollyVote

The most recent PollyVote expects Clinton to gain 52.4% of the two-party vote. That is, Polly's forecast is 1.4 points above her polling numbers. Again, a look at the poll's sampling error shows that this deviation is negligible.

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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