  The range of a quantum measurement is the set of outcome probability
distributions that can be produced by varying the input state. We introduce
data-driven inference as a protocol that, given a set of experimental data as a
collection of outcome distributions, infers the quantum measurement which is,
i) consistent with the data, in the sense that its range contains all the
distributions observed, and, ii) maximally noncommittal, in the sense that its
range is of minimum volume in the space of outcome distributions. We show that
data-driven inference is able to return a unique measurement for any data set
if and only if the inference adopts a (hyper)-spherical state space (for
example, the classical or the quantum bit).
  In analogy to informational completeness for quantum tomography, we define
observational completeness as the property of any set of states that, when fed
into any given measurement, produces a set of outcome distributions allowing
for the correct reconstruction of the measurement via data-driven inference. We
show that observational completeness is strictly stronger than informational
completeness, in the sense that not all informationally complete sets are also
observationally complete. Moreover, we show that for systems with a
(hyper)-spherical state space, the only observationally complete simplex is the
regular one, namely, the symmetric informationally complete set.
