Theory of Sensitivity Testing

[Rassis]

I'll have to think about that one. There is some value to doing that, the SD of the result for a fluctuation of the input is probably more telling in some regards than comparing single point values. However, if the fluctuation is small, it may not give me direction (y gets smaller as x gets larger, and vice versa).

When it comes to sensitivity of an output variable to each one of the input variables, only (de)increments make sense – in what magnitude and direction are concerned.

On the other hand, if you wish to find out the probability of an output variable assuming a certain minimum (or maximum value) – the so improperly called “risk” – then, the usual procedure is to build a simulation model, where each input random variable (the ones that you don’t control) is represented by a probability distribution (the one known as being the most representative). Variables which are deterministic in nature, assume a unique value (you control them). After hundreds or even thousands of repetitions are carried out (in each repetition, variables assume values picked randomly from their distributions and interact among them another), you calculate frequencies of the output data or check which theoretical distribution fits them the best. Only in this case statistical parameters such as a measure of variability (standard deviation, variance, coefficient of variability…) may adequately apply.