Why should correction values be better known than the measurand true value?

Since the beginning of the history of modern measurement science, the experimenters faced the problem of dealing with systematic effects, as distinct from, and opposed to, random effects. Two main schools of thinking stemmed from the empirical and theoretical exploration of the problem, one dictating that the two species should be kept and reported separately, the other indicating ways to combine the two species into a single numerical value for the total uncertainty (often indicated as 'error'). The second way of thinking was adopted by the GUM, and, generally, adopts the method of assuming that their expected value is null by requiring, for all systematic effects taken into account in the model, that corresponding 'corrections' are applied to the measured values before the uncertainty analysis is performed. On the other hand, about the value of the measurand intended to be the object of measurement, classical statistics calls it 'true value', admitting that a value should exist objectively (e.g. the value of a fundamental constant), and that any experimental operation aims at obtaining an ideally exact measure of it. However, due to the uncertainty affecting every measurement process, this goal can be attained only approximately, in the sense that nobody can ever know exactly how much any measured value differs from the true value. The paper discusses the credibility of the numerical value attributed to an estimated correction, compared with the credibility of the estimate of the location of the true value, concluding that the true value of a correction should be considered as imprecisely evaluable as the true value of any 'input quantity', and of the measurand itself. From this conclusion, one should derive that the distinction between 'input quantities' and 'corrections' is not justified and not useful.