You can model uncertain parameters within a model as random variables, and then run a large number of simulations to get a distribution of outcomes.
Modeling uncertainty between models (of which guessing the distribution of an uncertain parameter is an example) is harder to handle formally. But overall, it's not difficult to improve on the naive guess-the-exact-values-and-predict method.
You can model uncertain parameters within a model as random variables, and then run a large number of simulations to get a distribution of outcomes.
The usual error analysis provides an estimate of an error in the result in terms of error in the parameters. Any experiment used to test a model is going to rely on this kind of error analysis to determine whether the result of the experiment lies within the estimated error of the prediction given the uncertainty in the measured parameters.
For example, for an inverse-square law (like Newtonian gravity) you ...
Another month, another rationality quotes thread. The rules are: