Conditional probability and election odds


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It’s often said that people have trouble processing information about probabilities. Election odds are a perfect example.


For instance, all three of these claims are true:


1. Trump is overwhelmingly favored to win the GOP nomination.

2. If Trump is nominated, he’s expected to win the general election.

3. The Democrats are expected to win the 2020 general election.


How can that be? The simplest answer is that there’s about a 20% chance that someone other than Trump will be the GOP nominee, and in that case the Dems are strong favorites to win (as they should be in my view, as a non-Trump candidate would imply a civil war within the GOP.)



Thus Warren would be likely to win if she gets the nomination, but she’d be likely to lose if both she and Trump are nominated.  The Dems would do better with the second tier (Biden, Sanders, Buttigieg, Yang, etc.—but not Hillary.)


On the other hand, I would not take conditional probabilities of minor candidates too seriously, as the data may not be statistically significant when probabilities are low.  Nonetheless, I believe that a candidate from that group of four men would be more likely to win than Warren, even over Trump.  Warren looks like a weak candidate, which is why I expect the Dems to pick her.  They are dumb enough to fall into Trump’s trap, taking the Biden corruption allegations seriously.


PS.  Bill Weld as the GOP nominee?  LOL.  Who took that bet?


PPS.  If I weren’t so lazy I’d put money on Trump to be the GOP nominee.



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