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This question already has an answer here:

I have two (fully independent) measurements of the same quantity X. Each of them reports a measurement $X_{\sigma_L}^{\sigma_R}$, where $\sigma_L$ and $\sigma_R$ are the left and right uncertainties (asymmetric error bars).

In other words, if we call the measurements $A$ and $B$, and the subscripts $A$ and $B$ stand for the measurements, we have

$A_{\sigma_{L,A}}^{\sigma_{R,A}}$

$B_{\sigma_{L,B}}^{\sigma_{R,B}}$

Now, I need to calculate the difference between those measurements, $\Delta = A-B$. What will be $\sigma_{L,\Delta}$, $\sigma_{R,\Delta}$?

In other words, how do I propagate independent asymmetric error bars?

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marked as duplicate by Ben Crowell, Buzz, Kyle Kanos, Aaron Stevens, John Rennie Dec 26 '18 at 6:45

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Well, I think as long as you find a good argument for the solution you apply, you could do a couple of things. I think the easiest, which rather overestimates the errors is to use Gaussian error propagation with:

Be $s_\Delta$ the error of your difference, i.e. $$s_\Delta=\sqrt{\sigma_A+\sigma_B}.$$

The asymmetric uncertainty would be

$$s_{\Delta,L}=\left\{\begin{array}{lr}\sqrt{\sigma_{L,A}+\sigma_{R,B}},&&&\sigma_{L,A}>\sigma_{L,B}\\ \sqrt{\sigma_{R,A}+\sigma_{L,B}},&&&\sigma_{L,B}>\sigma_{L,A} \end{array}\right.$$

$$s_{\Delta,R}=\left\{\begin{array}{lr}\sqrt{\sigma_{R,A}+\sigma_{L,B}},&&&\sigma_{R,A}>\sigma_{R,B}\\ \sqrt{\sigma_{L,A}+\sigma_{R,B}},&&&\sigma_{R,B}>\sigma_{R,A} \end{array}\right.$$

So you find the maximum span between them. Other solutions are likely, possible, and feasible.

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