By monitoring and controlling a robot’s object-level reasoning, a metareasoning policy should improve the robot’s performance and avoid unsafe actions. Metareasoning might reduce the computational resources required for reasoning, or it might lead to more efficient utilization of those resources. Metareasoning might improve the reasoning process so that it generates better solutions that improve the robot’s performance. This chapter discusses ways to measure the performance of a metareasoning policy and approaches for designing the tests that are needed to collect evidence about a metareasoning policy’s performance. Assurance cases can be useful to supporting claims about a metareasoning policy’s performance and safety. In addition, we should document our testing in a way that supports replicability so that others can repeat our tests if needed. This chapter also describes techniques for visualizing metareasoning, which can be useful for understanding and improving the metareasoning approach.