Statistical quality-control principles include

• A. control charts, histograms, and run charts
• B. random sampling, inspection, and rework
• C. Acceptance sampling, process control, and designed studies
• D. data-driven quality improvements

Answer: (C)

Statistical quality-control principles focus on keeping processes stable and decisions data-driven. Acceptance sampling gives a practical way to judge a batch by inspecting a subset, setting rules for accepting or rejecting a lot while balancing risk for both producer and consumer. Process control centers on monitoring a process over time with control charts to separate normal, common variation from special causes, so you can intervene before defects accumulate. Designed studies (designed experiments) let you deliberately vary process factors to learn how they affect quality, enabling optimization and reduction of variation.

Tools like control charts, histograms, and run charts are useful analytics, but they’re specific instruments rather than the overarching principles. Random sampling, inspection, and rework reflect methods and remedies rather than the core principles. Data-driven quality improvements describe the goal, not a defined principle set.