I've assumed, I could add circuit error, at least to good approximation. For instance, two 5%, 100 resistors in series would have a series value of 200 +/- 10 Ohms. But is it good enough for large errors from many contributing factors? There are cases where you might want to know the expected error from many contributions is series, parallel, and as error products such as a current source acting through a resistor. Say we can model the distribution of component values as a Gaussian probability distribution about a nominal value. A 5% quoted spec might mean that 95%, or two standard deviations worth of parts, will fall within +/- 5% of nominal. Would the expected distribution of these two seried resistors simply be 5% at two standards or does the error value combine differently. Noise, as you might recall, combines as the square root, rather than directly, so I wonder about combining error. Edit: I don't mean to nit-pick, but I know a Gaussian distribution itself is somewhat non-physical, as this means some non-vanishingly small number of resistors would have negative resistance, but I think a Gaussian distribution should be sufficient for the usual error values encountered.