Love statistics? Read here what went wrong with the USA presidential election polls

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.

and this one, which I think is the main reason for getting things so wrong:

 

One of the reasons for poll inaccuracy in the US election, besides some people for whatever reason don´t say the truth is, that the “winner takes it all” effect makes predictions even less easier. A 1% difference in one state can lead to a complete shift of a state and influence the whole outcome very heavily. Hillary had more voters just like Al Gore vs Bush.

The Brexit referendum was not a normal election and therefore also harder to predict (No good historical data and everyone was like a first time voter on this matter). People who for decades vote for the same party stabilize predictions.

My opinion is that the statistics applied function very well with individuals. But here we don’t have just individuals, we have the “winner takes it all” approach. And this biased the entire thing.

 


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