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LA Times/USC poll in California: Clinton with whopping advantage


LA Times/USC released the results of a new poll, in which respondents from California were asked for whom they will vote: Democrat Hillary Clinton or Republican Donald Trump.

LA Times/USC poll results




According to the results, 56.0% of participants would give their vote to former First Lady Hillary Clinton, while 30.0% plan to vote for real estate developer Donald Trump.

The poll was conducted from May 19 to May 31. A total of 1500 registered voters responded. If one takes into account the poll's sampling error of +/-2.9 percentage points, the difference in voter support is statistically significant.

Putting the results in context

As any other method, polls are subject to bias. As a result, don't rely too much on the results of an individual poll. Instead of relying on results from single polls, forecasting research recommends to look at combined polls or, even better, the combined PollyVote forecast that uses different methods and data.

For the following analysis, we translate Clinton's and Trump's raw poll numbers into shares of the two-party vote. The corresponding figures are 65.1% for Clinton and 34.9% for Trump. On September 12 Clinton received only 34.9% in the LA Times/USC poll and Trump received only 0.0%.

Comparison to other polls

If we look at an average of California polls, Clinton's current two-party vote share is at 64.1%. Relative to her numbers in the LA Times/USC poll Clinton's poll average is 1 percentage point worse. This difference is within the poll's error margin, which means that the poll is not an outlier.

Results compared to the combined PollyVote prediction

The latest PollyVote predicts Clinton to gain 61.4% of the two-party vote in California. This means that Polly's combined forecast is 3.7 points below her polling numbers. Again, a look at the poll's error margin indicates that this deviation 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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