Final answer:
Sensitivity analysis uncertainty examines the impact of input variability on the output uncertainly in models or systems, often using summation of individual percent uncertainties when uncertainties are small.
Step-by-step explanation:
Sensitivity analysis uncertainty refers to assessing how the uncertainty in the output of a model or system is affected by variations in the input parameters. This concept is crucial in scientific measurements and calculations because all measurements have some level of uncertainty due to the varying quality of measuring tools and the limitations of the measurement process itself. When calculations are made from measured values, such as multiplying the length and width to find an area, the uncertainties in these measurements propagate through the calculation.
In cases where measurements have small uncertainties (a few percent or less), the percent uncertainty can often be determined by adding the percent uncertainties of the individual measurements. For instance, if a measured value A has an uncertainty represented as ±ΔA (delta A), you could express the measurement as A + ±ΔA. The overall uncertainty in a calculation involving multiplication or division can then be estimated by summing the individual percent uncertainties.