When we only have partial information about the probability distribution, i.e., when several different probability distributions are consistent with our knowledge, then it makes sense to select a distribution with the largest entropy. In particular, when we only know that the quantity is located within a certain interval -- and we have no information about the probability of different values within this intervals -- then it is reasonable to assume that all these values are equally probable, i.e., that we have a uniform distribution on this interval. The problem with this idea is that if we apply it to the same quantity after a non-linear rescaling, we get a different (non-uniform) distribution in the original scale. In other words, it seems that the results of applying the Maximum Entropy approach are rather arbitrary: they depend on what exactly scale we apply them to. In this paper, we show how to overcome this subjectivity: namely, we propose to take into account that, due to measurement inaccuracy, we always have finitely many possible measurement results, and this finiteness makes the results of applying the Maximum Entropy approach uniquely determined.