 As proponents of model-based accounts stress, inferences from instrument indications to measurement outcomes are nontrivial and depend on a host of theoretical and statistical assumptions about the object being measured, the instrument, the environment and the calibration process. Measurement outcomes are often obtained through statistical analysis of multiple indications, thereby involving assumptions about the shape of the distribution of indications and the randomness of environmental effects (Bogen and Woodward 1988: 307–310). Measurement outcomes also incorporate corrections for systematic effects, and such corrections are based on theoretical assumptions concerning the workings of the instrument and its interactions with the object and environment. For example, length measurements need to be corrected for the change of the measuring rod’s length with temperature, a correction which is derived from a theoretical equation of thermal expansion. Systematic corrections involve uncertainties of their own, for example in the determination of the values of constants, and these uncertainties are assessed through secondary experiments involving further theoretical and statistical assumptions. Moreover, the uncertainty associated with a measurement outcome depends on the methods employed for the calibration of the instrument. Calibration involves additional assumptions about the instrument, the calibrating apparatus, the quantity being measured and the properties of measurement standards (Rothbart and Slayden 1994; Franklin 1997; Baird 2004: Ch. 4; Soler et al. 2013). Another component of uncertainty originates from vagueness in the definition of the measurand, and is known as “definitional uncertainty” (Mari and Giordani 2013; Grégis 2015). Finally, measurement involves background assumptions about the scale type and unit system being used, and these assumptions are often tied to broader theoretical and technological considerations relating to the definition and realization of scales and units.