I am interested in why the 2016 presidential election polls did a poor job in forecasting the result. One hypothesis I heard is that many of the polls conducted before the election were correlated, and a sensitivity analysis can be done to the datasets to figure this out.
My understanding of sensitivity analysis is the study of how errors are propagated and changed from the inputs to the output. But how can this be applied to election polls? The connection between the two is not obvious to me.
More specifically, given a series of poll datasets before the election, how can I do a sensitivity analysis to figure out how much impact does the correlation between polls have on the quality of the forecast of the election result?