Scientific code is often complex, resource-intensive, and sensitive to performance issues, making accurate timing and benchmarking critical for optimising performance and ensuring reproducibility. However, benchmarking scientific code presents several challenges, including variability in input data, hardware and software dependencies, and optimisation trade-offs. In this talk, I discuss the importance of timing and benchmarking for scientific code and outline strategies for addressing these challenges. Specifically, I emphasise the need for representative input data, controlled benchmarking environments, appropriate metrics, and careful documentation of the benchmarking process. By following these strategies, developers can effectively optimise code performance, select efficient algorithms and data structures, and ensure the reliability and reproducibility of scientific computations.