Physicists and engineers often have to calculate the uncertainty in a derived quantity.

For instance, a test engineer measures two angles. The uncertainty (or error) in these measurements appears to be +/- 1 degree. Subsequently, the engineer calculates the sum of these two angles. This sum is a derived quantity. But note that this derived quantity is composed of two measurements each having their own uncertainties, so what is the uncertainty (or error) in this derived quantity? In other words, how propagates the uncertainty from the measured quantities (the two angles) to a derived quantity (sum of two angles). For calculating the uncertainty in this derived quantity, physicists and engineers rely on what is called the addition in quadrature procedure.

The addition in quadrature procedure provides an estimate of the Standard Deviation Of the Mean (SDOM), which is a quantification of the uncertainty in a derived quantity. This SDOM, in turn, can be used for constructing confidence intervals for the derived quantity.

Bootstrap methods (such as the bias-corrected bootstrap method) provide another way of obtaining confidence intervals for the derived quantity.

The following R code shows how to compute confidence intervals for a derived physical quantity with (1) the addition in quadrature procedure, and (2) the bias-corrected bootstrap method. The code demonstrates that these two methods usually yield very similar confidence intervals.

Continue reading Error propagation in R: addition in quadrature versus the bootstrap method