The interesting part of this is almost everyone was wrong in this (though when you're talking the realm of probability, it's hard to actually say they were wron

The interesting part of this is almost everyone was wrong in this (though when you're talking the realm of probability, it's hard to actually say they were wrong). In my statistics classes we often talk about bias and biased estimates, that you calculate something in such a way that it's systematically different than the true underlying population value. (I.E. if Trump's true likelihood of winning the presidency was X - we always resulted in estimates that were X minus some value below). Long story short - it's cool to see in the real world :). You can have the fanciest models and math, but if the underlying data and assumptions you're working with aren't representative of the population, then it's all for naught.