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California: Clinton with overwhelming advantage in KABC/SurveyUSA*KABC/SUSA* poll

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Results of a new poll conducted by KABC/SurveyUSAKABC/SUSA were spread. The poll asked participants from California for whom they will vote: Republican nominee Donald Trump or Democrat nominee Hillary Clinton.

KABC/SurveyUSAKABC/SUSA poll results
57

Clinton

32

Trump

Of those who replied, 57.0% said that they intend to vote for former New York Senator Hillary Clinton, while 32.0% said that they would give their vote to real estate developer Donald Trump.

This poll was conducted from September 8 to September 11, among a random sample of 678 likely voters. If one takes into account the poll's margin of error of +/-3.8 percentage points, the difference in voter support is statistically significant.

Putting the results in context

Individual polls often include large errors, and should be interpreted with caution. Rather than trusting the results from single polls, the evidence-based approach is to look at combined polls or, even better, a combined forecast that relies on forecasts from different methods, each of which draws upon different data.

For the following analysis, we translate Clinton's and Trump's raw poll numbers into shares of the two-party vote. This procedure results in figures of 64.0% for Clinton and 36.0% for Trump.

Results vs. other polls

An average of recent polls in California has Clinton at 64.1% of the two-party vote. This value is 0.1 percentage points higher than her respective numbers in the KABC/SurveyUSAKABC/SUSA poll. This deviation is within the poll's sampling error, which suggests that the poll is not an outlier.

Comparison to the combined PollyVote

The current PollyVote forecasts Clinton to gain 61.3% of the two-party vote in California. Hence, the PollyVote forecast is 2.7 points below her polling numbers. Again, a look at the poll's margin of error suggests 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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