How Data Failed Us in Calling an Election

10DATA-1478727027137-master768.jpg

Michigan Democratic Party members in Flint studying precinct results on Tuesday. Virtually all major vote forecasters put Hillary Clinton’s chances of winning in the range of 70 to 99 percent. Credit: Brittany Greeson for The New York Times

 It was a rough night for number crunchers. And for the faith that people in every field — business, politics, sports and academia — have increasingly placed in the power of data.
Donald J. Trump’s victory ran counter to almost every major forecast — undercutting the belief that analyzing reams of data can accurately predict events. Voters demonstrated how much predictive analytics, and election forecasting in particular, remains a young science: Some people may have been misled into thinking Hillary Clinton’s win was assured because some of the forecasts lacked context explaining potentially wide margins of error.
“It’s the overselling of precision,” said Dr. Pradeep Mutalik, a research scientist at the Yale Center for Medical Informatics, who had calculated that some of the vote models could be off by 15 to 20 percent.
Virtually all the major vote forecasters, including Nate Silver’s FiveThirtyEight site, The New York Times Upshot and the Princeton Election Consortium, put Mrs. Clinton’s chances of winning in the 70 to 99 percent range. Continue reading
Source

Advertisements
Tagged with:
Posted in Knowledge Structures

by Hugh McLeod

Follow LIS 653 Knowledge Organization on WordPress.com
Pratt Institute School of Information
%d bloggers like this: