[stinkbeetle]

You misunderstand my point. I am talking about the actual election that happened where these many random events that could have resulted in a different outcome did not happen. I was being a bit facetious maybe in my point. But the point is that the thing that is to be predicted is the actual real event that occurs in this universe. Silver made a prediction, and it was wrong.

"Oh but it was only a 70% prediction"

You can't 70% win an election. Silver's prediction was that Clinton would win, but he was not super confident about it. The prediction was wrong. He was right to not be super confident about it, but the prediction of who would win was still wrong.

[zaphar]

Statistical likelihood is a measurement of the known data at the time. If you engage with the content otherwise then it's on you if you have the wrong takeaway. No one who makes a prediction based on a statistical model is going to be right every time. That doesn't mean it's not right to make a prediction. The statistical modeling can help you to be correct more often than not. And if you were going to be truly fair you would note that Nate in fact repeatedly said that it was still very much possible for Trump to win but that the current known polling data and other factors in his model pointed to a loss.

538's own post-mortem's on the event highlight that Trump was a very unusual candidate running in a very unusual election and as such the model was missing a lot of important information. They learned from the experience and adjusted the model going forward. Anyone complaining about that event is really just highlighting that they don't understand how statistical modeling works and are upset about how the model misled them or others which isn't Nate or 538's fault and is entirely on the consumer of their reporting. It's not like they didn't try to educate their consumers in their reporting.

[stinkbeetle]

I know what statistical likelihood is. I don't have a problem with them using a model or models and doing some statistics on it to develop these predictions, or even necessarily with the way they report their predictions as a % chance to win. I have a problem with the insinuation that "70% Clinton" is somehow a prediction of a singular real event or that Trump winning is consistent with said prediction "because if we held another 99 of those 2016 elections then Clinton probably would have won about 70 of them therefore I was right".

The prediction is for one single outcome at one point in time. The prediction can not be that Clinton 70% wins it, or wins it 70 out of every 100 times because there is no 100 2016 elections. Those things may apply to his mathematical models, but obviously the models are attempting to predict the real world. Try to weasel out of it as much as you like, but the prediction was that Clinton would win, and the prediction was wrong.

"Oh he was only giving the odds for his model, you don't understand it's your fault he mislead you" -- no. Every analyst and pundit has a model or a system, obviously nobody thinks any of them can see the future. Nate Silver was very explicitly predicting the outcome of the election. As you can see from all his commentary articles that came out along with the numbers.

And yes, 538's vaunted models and data science fell over when encountering situations that had not been seen or anticipated or built on before, obviously. We didn't need Einstein or even Nate Silver to tell us that. That's the problem isn't it. All this hamming up of "data science" and "mathematical models" is meaningless. Your data and math can be perfect and correct, but if they fail to provide an understanding of the world, then they are perfectly useless.

[zaphar]

You are asserting an insinuation that 538 never made. That is the disconnect here.

[stinkbeetle]

No I'm not.

[jscomino]

Just want to say, I appreciate your pragmatic perspective on this. Nate Silver had one job: Predict who would win. And he failed at that. With lots of hand waving he can excuse himself but at the end of the day his visitors wanted an answer and he gave them the wrong answer.