  In statistical modeling we strive to specify models that resemble data
collected in studies or observed from processes. Consequently, distributional
specification and parameter estimation are central to parametric models.
Graphical procedures, such as the quantile-quantile (Q-Q) plot, are arguably
the most widely used method of distributional assessment, though critics find
their interpretation to be overly subjective. Formal goodness-of-fit tests are
available and are quite powerful, but only indicate whether there is a lack of
fit, not why there is lack of fit. In this paper we explore the use of the
lineup protocol to inject rigor to graphical distributional assessment and
compare its power to that of formal distributional tests. We find that lineups
of standard Q-Q plots are more powerful than lineups of de-trended Q-Q plots
and that lineup tests are more powerful than traditional tests of normality.
While, we focus on diagnosing non-normality, our approach is general and can be
directly extended to the assessment of other distributions.
