I wonder if it’s fair to start asking questions about how forecasting influenced the 2016 election: https://solomonmg.github.io/projects/1_project/ …https://twitter.com/NateSilver538/status/1076521277756788736 …
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I read the text and footnotes—few previous datapoints with big gaps between (every four years) to base a model on, and one in three is huge odds. Almost nobody reads footnotes. Even 60/40 looks like a landslide because we have a lifetime of being trained to read polls, not odds.
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So the issue is how things are interpreted and what can be done to not have them be interpreted erroneously and how prediction, in general and however presented, affects behavior. All important.
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People generally interpret election forecast probabilities as margin of victory forecasts with 100% likelihood.
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10% in our study
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