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?(adsbygoogle = window.adsbygoogle || []).push({});

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.

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# Adding accumulated error

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