The outcome of an analytical measurement (application of the chemical measurement process), or value attributed to a measurand. This may be the result of direct observation, but more commonly it is given as a statistical estimate derived from a set of observations. The distribution of such estimates (estimator distribution) characterizes the chemical measurement process, in contrast to a particular estimate, which constitutes an experimental result. Additional characteristics become evident if we represent \(\hat{x}\) as follows: \[\hat{x} = \tau + e = \rlap{\underbracket{\phantom{\tau + \Delta}}_\mu }\tau + \overbracket{\Delta+\delta}^{e} = \mu + \delta\] The true value, \(\tau \), is the value \(x\) that would result if the chemical measurement process were error-free. The error, \(e\), is the difference between an observed (estimated) value and the true value; i.e. \(e=\hat{x}- \tau \) (signed quantity). The total error generally has two components, bias (\(\mathit{\Delta}\)) and random error (\(\delta \)), as indicated above. The limiting mean, \(\mu\), is the asymptotic value or population mean of the distribution that characterizes the measured quantity; the value that is approached as the number of observations approaches infinity. Modern statistical terminology labels this quantity the expectation value or expected value, \(E(\hat{x})\). The bias, \(\mathit{\Delta}\), is the difference between the limiting mean and the true value; i.e. \(\mathit{\Delta} = \mu - \tau \) (signed quantity). The random error, \(\delta \), is the difference between an observed value and the limiting mean; i.e. \(\delta = \hat{x} - \mu \) (signed quantity).