How to Make Better Predictions When You Don’t Have Enough Data

When Donald Trump first declared his candidacy for President of the United States, most analysts predicted that he has an incredibly small chance of becoming the Republican nominee. Probably the most prominent of these was Nate Silver from FiveThirtyEight. He estimated that Trump had a 2% chance of winning the nomination. This estimation was based on multiple significant historic data points about past candidates, such as the background they came from, whether they were widely endorsed by the party, and their past successes and failures. This is a standard prediction approach based on the underlying assumption that what you are trying to predict (Trump) is comparable to its historical antecedents (past GOP candidates) and thus can be evaluated according to their performance. However, as it is clear to us now,…


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