> if the election were repeated an infinity number of times, wouldn't the same candidate win each time? It depends on what you mean by "repeated an infinite nu

> if the election were repeated an infinity number of times, wouldn't the same candidate win each time? It depends on what you mean by "repeated an infinite number of times". First, there's a problem with the framing of that statement, because you can't literally repeat something an infinite number of times. You can repeat something indefinitely, and observe the behavior as the number of repetitions increases without bound, but that's not quite the same thing. (I realize this sounds pedantic, but if you want to understand the underlying interpretation of probability, it's an important distinction to be aware of). So, let's say you asked, "if the election were repeated over and over again, without bound, what would the outcomes be?" And the answer to that is... well, it depends on who you ask. Setting aside the "fate vs. free will" philosophical question, different statisticians would answer that question differently, depending on which interpretation of probability they use. There are three main interpretations of probability, though these days, people pretty much only ever use two (frequentist and Bayesian). And you could construct either a frequentist or a Bayesian argument that the same candidate would always win, or that a different candidate could win each time. Think of the election as a function - some unknown inputs go in, and an outcome goes out. What are the inputs to that function? (That's a rhetorical question - we're not really sure what makes every individual voter vote, and what disruptions could happen to nudge each person to vote a different way, or to not make it to the polls that day, or for their ballot to get lost in the mail, etc.). Congratulations, you have a random variable[0]! Now all you have to do is decompose it to determine the underlying components and the true distribution they draw from[1]. I realize that's not a very satisfying answer, but the underlying issue ("what does probability mean") is not a simple question, and statisticians have been debating it for over a century. [0] which is, confusingly, neither random nor a variable. [1] Which is itself controversial way of putting it, because some statisticians would argue that there is no "true" distribution, or that, if there is, we can never actually know it.