Mixed effects models are used routinely to share information across groups and to account for data dependence. The statistical properties of such models are often quite good on average across groups, but may be poor for any specific group. For example, commonly-used confidence interval procedures may maintain a target coverage rate on average across groups, but
have near zero coverage rate for a group that differs substantially from the others. In this talk, we review some basic mixed effects modeling tools, discuss their group-specific properties, and present some new tools for multiple testing and inference problems that permit information sharing across groups while controlling group-specific frequentist error rates.