Except for purely nonparametric methods, statistical methods depend on assumptions about the distribution of the data studied. While these assumptions are easily checked for a single univariate dataset with diagnostic plots, in the massively univariate model used with functional MRI (fMRI) it is impractical to check with a massive number of plots. In previous work we have demonstrated how to diagnose model assumptions and lack-of-fit for single-subject fMRI models using a working assumption of independent errors; our work depended on images and time series of summary statistics that, when simultaneously viewed dynamically, identify problem scans and voxels. In this article we extend our previous work to account for temporal autocorrelation in single-subject models and show how analogous methods can be used on group models where multiple subjects are studied. We apply these methods to the single-subject Functional Image Analysis Contest (FIAC) data and find several anomalies, but none that appear to invalidate the results for that subject. With the group FIAC data we find one subject (and possibly two more) that demonstrate a different pattern of activity. None of our conclusions would be arrived at by simply looking at images of t statistics, demonstrating the importance of model assessment through exploration of the data and diagnosis of model assumptions.