There were at least two major errors committed by some of the data scientists who helped design the prediction algorithms. First, they assumed that if the odds of being off by nearly seven points in Wisconsin were low, the odds of a comparable error in other critical states like Michigan and Pennsylvania were tiny. In fact, polling problems in one state were correlated with mistakes in other, similar states. Assuming that polls were entirely independent of each other—rather than reflecting the same reactions to the same issues—produced overconfidence in Clinton’s lead.
Second, prediction algorithms failed to register the record number of undecided voters as a warning sign. Because so many voters were on the fence right up to Election Day—and would end up breaking strongly for Trump—Clinton’s margins were much less safe than they appeared.
“It was staring us right in the face,” says Rachel Bitecofer, a professor of political science at Christopher Newport University. Had there been more polls in the closely contested states just before Election Day, she suggests, analysts might have picked up on the unusually high number of voters who decided to turn out at the last moment.