You can't straightforwardly multiply uncertainty from different domains to propagate uncertainty through a model. Point estimates of differently shaped distributions can mean very different things i.e. the difference between the mean of a normal, bimodal, and fat tailed distribution. This gets worse when there are potential sign flips in various terms as we try to build a causal model out of the underlying distributions.

You can't straightforwardly multiply uncertainty from different domains to propagate uncertainty through a model. Point estimates of differently shaped distributions can mean very different things i.e. the difference between the mean of a normal, bimodal, and fat tailed distribution. This gets worse when there are potential sign flips in various terms as we try to build a causal model out of the underlying distributions.

guesstimate propagates full distributions for you