Are you coming at this from a frequentist perspective, a Bayesian perspective, or some other formulation of probability?
From a frequentist perspective, it makes no sense to talk about probabilities of the outcomes of processes that can't be repeated, such as elections. So the question is then, "Why couldn't the polls predict a result?" And we know the answer: because the polls weren't precise enough. We already knew that.
From a Bayesian perspective, lack of knowledge is the same thing as nondeterminism in the underlying processes. So, to a Bayesian, you're just wrong; there was a 52/48 chance of the voter base waking up and instead voting for Kamala yesterday.
This is so confused. The probability models are designed to describe situations where cause and effect is not known.causes still exist whether you can repeat them in an experiment,
You are confusing logical models with real world decisions and actions.
I'm asking what you mean by "probability" and "chance", but it sounds like the answer is that you don't have any idea, because you've never studied statistics even to the point of taking an introductory class. At this point you've explicitly rejected foundational axioms of both frequentist probability and Bayesian probability, with no apparent awareness that this means you have rejected the entire field of statistics.
You’re missing the point. Axiomatic systems aim to be internally consistent. The question is whether they are good model of a real life situation. Your technical knowledge is distracting you from the more fundamental questions.
There is no sense in which Harris had a 50% chance and had an unlucky day. The only “chance” going on is how likely the poll sample represents the population. The math behind that assumes you have a genuine sample and ignores realities like preference falsification.
Please think and read charitably before making personal attacks. I generally take that as a sign you are acting in bad faith and I do not want to interact with you. Goodbye.
