I'm going to spend a couple of posts talking about some techniques I've been using while building and calibrating models, as they seem to be less well known than they might be. The first in this series is Sensitivity Analysis.

What is Uncertainty and Sensitivity Analysis

UA and SA are techniques to analyse the robustness of model outputs; that is, given a model, and certain assumptions about its input parameters (and their distributions), we would like to know how much we can trust the output. In general terms, Uncertainty Analysis gives us the amount of uncertainty (or variance) in the model output, while Sensitivity Analysis tells us how much of that variance is due to each of the input factors.