I glibly wrote a blog post Sunday saying how confident I was that data was pointing to a Hillary Clinton win. Pretty much all the polls were showing that; almost all serious political data analysts and models said the same.What went wrong?I’ll leave political reasons to others. From a data-science perspective, though, I’m interested in learning something from this data debacle. The key issue I see is not fully understanding uncertainty when it comes to forecasting elections – a good lesson for anyone who tries to make predictions from data.Things are pretty straightforward if you just want to measure public opinion: You get a random sample of the population, you ensure as best you can that it’s a representative sample, you ask unbiased questions and you do your statistical calculations.But there’s an extra piece when it comes to election forecasting: Measuring overall “public opinion” isn’t the same as measuring public opinion among people who are actually going to vote.So there are two complexities here, not just one. You not only need to make sure your sample properly reflects the public; you also need to make sure your model of “the public” accurately predicts who will turn out and vote. Some… Read full this story
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Election night: Whoa, was I was wrong have 250 words, post on www.computerworld.com at November 9, 2016. This is cached page on Vietnam Art News. If you want remove this page, please contact us.