 Two key aspects of the reliability of measurement outcomes are accuracy and precision. Consider a series of repeated weight measurements performed on a particular object with an equal-arms balance. From a realist, “error-based” perspective, the outcomes of these measurements are accurate if they are close to the true value of the quantity being measured—in our case, the true ratio of the object’s weight to the chosen unit—and precise if they are close to each other. An analogy often cited to clarify the error-based distinction is that of arrows shot at a target, with accuracy analogous to the closeness of hits to the bull’s eye and precision analogous to the tightness of spread of hits (cf. JCGM 2012: 2.13 &amp; 2.15, Teller 2013: 192). Though intuitive, the error-based way of carving the distinction raises an epistemological difficulty. It is commonly thought that the exact true values of most quantities of interest to science are unknowable, at least when those quantities are measured on continuous scales. If this assumption is granted, the accuracy with which such quantities are measured cannot be known with exactitude, but only estimated by comparing inaccurate measurements to each other. And yet it is unclear why convergence among inaccurate measurements should be taken as an indication of truth. After all, the measurements could be plagued by a common bias that prevents their individual inaccuracies from cancelling each other out when averaged. In the absence of cognitive access to true values, how is the evaluation of measurement accuracy possible?