Network sampling is a crucial technique for analyzing large or partially observable networks. However, the effectiveness of different sampling methods can vary significantly depending on the context. In this study, we empirically compare representative methods from three main categories: node-based, edge-based, and exploration-based sampling. We used two real-world datasets for our analysis: a scientific collaboration network and a temporal message-sending network. Our results indicate that no single sampling method consistently outperforms the others in both datasets. Although advanced methods tend to provide better accuracy on static networks, they often perform poorly on temporal networks, where simpler techniques can be more effective. These findings suggest that the best sampling strategy depends not only on the structural characteristics of the network but also on the specific metrics that need to be preserved or analyzed. Our work offers practical insights for researchers in choosing sampling approaches that are tailored to different types of networks and analytical objectives.