  We investigate the ability of individuals to visually validate statistical
models in terms of their fit to the data. While visual model estimation has
been studied extensively, visual model validation remains under-investigated.
It is unknown how well people are able to visually validate models, and how
their performance compares to visual and computational estimation. As a
starting point, we conducted a study across two populations (crowdsourced and
volunteers). Participants had to both visually estimate (i.e, draw) and
visually validate (i.e., accept or reject) the frequently studied model of
averages. Across both populations, the level of accuracy of the models that
were considered valid was lower than the accuracy of the estimated models. We
find that participants' validation and estimation were unbiased. Moreover,
their natural critical point between accepting and rejecting a given mean value
is close to the boundary of its 95% confidence interval, indicating that the
visually perceived confidence interval corresponds to a common statistical
standard. Our work contributes to the understanding of visual model validation
and opens new research opportunities.
