(p. B1) 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: . . .
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(p. B5) This week’s failed election predictions suggest that the rush to exploit data may have outstripped the ability to recognize its limits.
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Beyond election night, there are broader lessons that raise questions about the rush to embrace data-driven decision-making across the economy and society.
The enthusiasm for big data has been fueled by the success stories of Silicon Valley giants born on the internet, like Google, Amazon and Facebook. The digital powerhouses harvest vast amounts of user data using clever software for search, social networks and online commerce. Data is the fuel, and algorithms borrowed from the tool kit of artificial intelligence, notably machine learning, are the engine.
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The danger, data experts say, lies in trusting the data analysis too much without grasping its limitations and the potentially flawed assumptions of the people who build predictive models.
For the full story, see:
STEVE LOHR and NATASHA SINGER. “How Data Failed Us in Calling an Election.” The New York Times (Sat., NOV. 10, 2016): B1 & B5.
(Note: ellipses added.)