(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
The election outcome is a surprise in that it contradicts two pieces of information: Pre-election polls and early-voting tallies. We knew that each of these indicators could be flawed (polls because of differential nonresponse; early-voting tallies because of extrapolation errors), but when the two pieces of evidence came to the same conclusion, they gave us a false feeling of near-certainty.
In retrospect, a key mistake in the forecast updating that Kremp and I did, was that we ignored the correlation in the partial information from early-voting tallies. Our model had correlations between state-level forecasting errors (but maybe the corrs we used were still too low, hence giving us illusory precision in our national estimates), but we did not include any correlations at all in the errors from the early-voting estimates. That’s why our probability forecasts were, wrongly, so close to 100% (as here).
The post Election forecasting updating error: We ignored correlations in some of our data, thus producing illusory precision in our inferences appeared first on Statistical Modeling, Causal Inference, and Social Science.
Please comment on the article here: Statistical Modeling, Causal Inference, and Social Science
