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About Wrong
- The document discusses scientists failing to correctly predict the outcome of the 2016 US presidential election and lessons that can be learned from this. - It argues that predictions alone are not enough in science and that models are needed to provide a framework for understanding how and why predictions are made. Good models make multiple testable predictions, not just about the event being predicted. - Bayesian statistics provides a systematic method for comparing models based on their predictive performance on past data and updating beliefs about which model is best based on new evidence. Being wrong for the right reasons means choosing the model that evidence previously supported, even if it ends up making an incorrect prediction.
Detailed Information
| Author: | ['TBP_Think_Tank'] |
|---|---|
| Publication Year: | 2016 |
| Pages: | 11 |
| Language: | English |
| Format: | |
| Price: | FREE |
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