What is the key characteristic of a normal distribution on a control chart?

A normal distribution on a control chart is characterized by a central mean with bell-shaped symmetry. This shape indicates that most of the data points are concentrated around the mean, with probabilities tapering off equally as you move away from the mean in both directions. In a control chart context, this symmetry is crucial as it reflects the expected variation of a process under stable conditions. The majority of data points typically fall within three standard deviations from the mean, which helps in identifying any outliers or deviations that may signal issues in the process being monitored.

The other characteristics do not align with the properties of a normal distribution. For instance, clustering data points at one end would indicate skewness rather than the balanced shape typical of a normal distribution. The presence of common outliers also contradicts the expectations of a normal distribution, which minimizes their occurrence within typical processes. Lastly, stating that the mean has no significant significance undermines the importance of the mean in understanding the central tendency of normally distributed data. Overall, the central mean with bell-shaped symmetry is essential for interpreting and analyzing the data accurately in control charts.