What criteria must be met to identify a non-random pattern in data?

Identifying a non-random pattern in data is a critical aspect of quality improvement and process control. The correct choice highlights key elements that indicate a systematic issue within the data set. These elements include shifts, trends, an excessive number or insufficient number of runs, and astronomical points.

A shift refers to a sustained change in the data, where the values consistently start to fall into a new range. A trend indicates a consistent increase or decrease over time, rather than random fluctuations. The concept of runs refers to a sequence of similar data points, and analyzing the number of runs can reveal whether the data is behaving randomly or showing a type of clustering that indicates a non-random pattern. Lastly, astronomical points are data points that fall far outside the expected range, potentially indicating anomalies or changes in the process.

The other options do not reflect the comprehensive criteria needed to determine a non-random pattern effectively. Simply considering a trend, for example, would not account for shifts or the distribution of runs, which are essential for a more thorough understanding of the data's behavior. Thus, option B encompasses the necessary breadth of analysis needed for proper evaluation of data patterns.