Database workloads consist of <i>mixes</i> of queries that run concurrently and interact with each other. In this paper, we demonstrate that query interactions can have a significant impact on database system performance. Hence, we argue that it is important to take these interactions into account when characterizing workloads, designing test cases, or developing performance tuning algorithms for database systems. To capture and model query interactions, we propose using an experimental approach that is based on sampling the space of possible interactions and fitting statistical models to the sampled data. We discuss using such an approach for database testing and tuning, and we present some opportunities and research challenges.