Data science predicts election winner!

What’s good about data science is that it gives us objective metrics for assessing the level of confidence we should place in any given prediction. From a statistical analysis perspective, predictions inspire confidence if they can survive every data-driven effort to invalidate them. If all models confirm a specific forecast — even if they differ widely in data sources, sampling techniques, and feature-engineering models — they probably hit the predictive bull’s-eye dead-on. But if the latest models start to call the prediction into doubt, confidence in the forecast may wilt quickly and irretrievably. Yet the main reasons why confidence in a prediction wanes aren’t always statistical in nature. Confidence is a psychological and even sociological phenomenon that shapes how predictive modeling results are interpreted by stakeholders. Merely trying to poke holes…


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