On the previous page, we introduced the measurement function, and saw that input quantities are sensitive to errors from one, or more, error effects.<\/p>\r\n\r\n\r\n\r\n
However, when conducting an uncertainty analysis, there is an additional factor that we must also consider: the extent to which the measurement function describes the true physical state of the measurement process. We can account for this factor by including \u2018plus zero\u2019 term in the measurement function:<\/p>\r\n\r\n\r\n\r\n
y=f(x_1,x_2,x_3\u2026xn)+0<\/span><\/p>\r\n\r\n\r\n\r\n Here, this \u2018plus zero\u2019 term does not alter the value of the measurand, but will have an associated uncertainty in recognition of the fact that all measurement functions are approximations to the physical process they describe. In other words, this term considers the extent to which the equality of the measurement function may not hold. For example:<\/p>\r\n\r\n\r\n\r\n Once we have a clear picture of the extent to which the measurement function describes the true physical state of the measurement process and the effects that influence each input quantity, we can determine the uncertainty in the measurand through the process of uncertainty analysis. We\u2019ll examine uncertainty analysis in detail later in a separate lesson, but before we do, it will be useful to examine the benefits of representing the measurement function graphically in the form of an uncertainty analysis tree, which we\u2019ll look at on the next page.<\/p>\r\n","protected":false},"excerpt":{"rendered":"\r\n