Performance measurement and analysis are commonly carried out tasks for high-performance computing applications. Both sampling and instrumentation approaches for performance measurement can capture hardware performance counter (HWPC) metrics to asses the software’s ability to use the functional units of the processor. Since the measurement software usually executes on the same processor, it necessarily competes with the target application for hardware resources. Consequently, the measurement system perturbs the target application, which often results in runtime overhead. While the runtime overhead of different measurement techniques has been previously studied, it has not been thoroughly examined to what extent HWPC values are perturbed by the measurement process. In this paper, we investigate the influence of the two widely-used performance measurement systems HPCToolkit (sampling) and Score-P (instrumentation) w.r.t. their influence on HWPC. Our experiments on the SPEC CPU 2006 C/C++ benchmarks show that, while Score-P’s default instrumentation can massively increase runtime, it does not always heavily perturb relevant HWPC. On the other hand, HPCToolkit shows no significant runtime overhead, but significantly influences some relevant HWPC. We conclude that for every performance experiment sufficient baseline measurements are essential to identify the HWPC that remain valid indicators of performance for a given measurement technique. Thus, performance analysis tools need to offer easily accessible means to automate the baseline and validation functionality.