Numerous software systems are highly configurable and provide a myriad of configuration options that users can tune to fit their functional and performance requirements (e.g., execution time). Measuring all configurations of a system is the most obvious way to understand the effect of options and their interactions, but is too costly or infeasible in practice. Numerous works thus propose to measure only a few configurations (a sample) to learn and predict the performance of any combination of options’ values. A challenging issue is to sample a small and representative set of configurations that leads to a good accuracy of performance prediction models. A recent study devised a new algorithm, called distance-based sampling, that obtains state-of-the-art accurate performance predictions on different subject systems. In this paper, we replicate this study through an in-depth analysis of x264, a popular and configurable video encoder. We systematically measure all 1,152 configurations of x264 with 17 input videos and two quantitative properties (encoding time and encoding size). Our goal is to understand whether there is a dominant sampling strategy over the very same subject system (x264), i.e., whatever the workload and targeted performance properties. The findings from this study show that random sampling leads to more accurate performance models. However, without considering random, there is no single “dominant" sampling, instead different strategies perform best on different inputs and non-functional properties, further challenging practitioners and researchers.