Ok,

I understand what you understand - nothing special meant here. Let me make things less abstract.

Basically what Monte Carlo does is to generate "outcomes", e.g. where a bullet ends, for, varying conditions - the input parameters. The input parameters are varied according to a certain probability distribution. If for example a shooter is able to aim with an accuracy of 1 MoA then people like me model that as a bell-shaped (Gaussian) probability distribution that averages to 1 MoA mean inaccuracy. With this we acknowledge the fact that in some cases the shooter aims very well and in others badly.

In a Monte Carlo simulation then, the computer draws a random number and converts that to an inital horizontal and vertical aiming angle. This is done in such a way that average inaccuracy is 1 MoA. After the horizontal and vertical launch angles are chosen, the bullets trajectory is calculated.

If, for instance, in addition the effects of a non-constant wind is being studied, one might model that primitively as a wind distribution that is half of the time zero and the other half 1 m/s. In a Monte Carlo calculation then one first draws a random number that determines the launch angle, and an another random number is used to determine if there is wind or not in the trajectory calculation (with the afforementioned spreadsheet I deal on a more sophisticated way with non constant wind along a trajectory). Hence we end up with a set of bullet end coordinates that were affected by both wind and the shooters abilities.

In practise, the more inputs one generates according to a probability distribution, the less the details of a probability distribution matters. The distribution might as well be flat, meaning that the probability for a certain input value is constant in a certain range and zero elsewhere.