Recent research on quality assurance (QA) of configurable software systems (e.g., software product lines) proposes different analysis strategies to cope with the inherent complexity caused by the well-known combinatorial-explosion problem. Those strategies aim at improving efficiency of QA techniques like software testing as compared to brute-force configuration-by-configuration analysis. Sampling constitutes one of the most established strategies, defining criteria for selecting a drastically reduced, yet sufficiently diverse subset of software configurations considered during QA. However, finding generally accepted measures for assessing the impact of sample-based analysis on the effectiveness of QA techniques is still an open issue. We address this problem by lifting concepts from single-software mutation testing to configurable software. Our framework incorporates a rich collection of mutation operators for product lines implemented in C to measure mutation scores of samples, including a novel family-based technique for product-line mutation detection. Our experimental results gained from applying our tool implementation to a collection of subject systems confirms the widely-accepted assumption that pairwise sampling constitutes the most reasonable efficiency/effectiveness trade-off for sample-based product-line testing.