[stupac2]

I'm at least 55% serious with my post! How would one declare a simulation obviously wrong? Applied to the Rose Bowl, for example: it's pretty easy to imagine a Stanford win, even with trivial changes -- say, catching one of the three or four dropped first downs, or one of the multitude of dropped picks? That's not to say MSU didn't *deserve* to win, just that a simulation showing a Stanford win isn't, like, crazy or anything. (Assuming we think simulations/hypotheticals have any truth-value at all!)

That bullshit endzone PI call, for one.

But your point is completely right, it's actually impossible to say that a simulation is wrong from a sample size of 1. A simulation is telling you a range of possibilities (something they directly say in the results!) and as such any given actuality fits within the framework of the simulation. The question is how it performs over many trials, which is obviously something we can't actually know for many, many years.

[stupac2]

I'm at least 55% serious with my post! How would one declare a simulation obviously wrong? Applied to the Rose Bowl, for example: it's pretty easy to imagine a Stanford win, even with trivial changes -- say, catching one of the three or four dropped first downs, or one of the multitude of dropped picks? That's not to say MSU didn't *deserve* to win, just that a simulation showing a Stanford win isn't, like, crazy or anything. (Assuming we think simulations/hypotheticals have any truth-value at all!)

That bullshit endzone PI call, for one.

But your point is completely right, it's actually impossible to say that a simulation is wrong from a sample size of 1. A simulation is telling you a range of possibilities (something they directly say in the results!) and as such any given actuality fits within the framework of the simulation. The question is how it performs over many trials, which is obviously something we can't actually know for many, many years.