Source: http://stats.stackexchange.com/questions/245063/us-election-results-2016-what-went-wrong-with-prediction-models   Original question: First it was Brexit, now the US election. Many model predictions were off by a wide margin, and are there lessons to be learned here? As late as 4 pm PST yesterday (n.b. on 08.11), the betting markets were still favoring Hillary 4 to 1. I take it that the betting markets, with real money on the line, should act as an ensemble of all the available prediction models out there. So it’s not far-fetched to say these models didn’t do a very good job. I saw one explanation was voters were unwilling to identify themselves as Trump supporters. How could a model incorporate effects like that? One macro explanation I read is the rise of populism. The question then is how could a statistical model capture a macro trend like that? https://www.foreignaffairs.com/articles/2016-10-17/power-populism Are these prediction models out there putting too much weight on data from polls and sentiment, not enough from where the country is standing in a 100 year view? I am quoting a friend’s comments. Even if a different response won, I like these two more: The USC/LA Times poll has some accurate numbers. They predicted Trump to be in the lead….