Which principle is used to sort measurement influences by magnitude and improve the largest variations?

The Pareto principle, also known as the 80/20 rule, is foundational in identifying and prioritizing factors that contribute to a significant amount of variation in a process or measurement. This principle suggests that roughly 80% of the effects come from 20% of the causes, indicating that by focusing on the most influential factors, one can potentially achieve a substantial improvement in the process or measurement.

By applying the Pareto principle, a calibration technician can effectively sort and prioritize measurement influences according to their impact. This allows for targeted investigations and improvements on those factors that contribute the most significant variations, leading to more efficient calibration processes and better overall measurement accuracy. This targeted approach not only saves time and resources but also helps in achieving more stable and reliable results, essential for maintaining quality in calibration practices.

In contrast, while the Bell curve principle relates to the distribution of data, the statistical significance principle focuses on determining whether the results of a study can be attributed to any specific cause, and the Lean principle is centered on minimizing waste and improving efficiency. Although these concepts are valuable in their own contexts, they do not specifically address the method of sorting measurement influences by their magnitude to focus on the largest variations, as the Pareto principle does.