What could indicate a special cause in a dataset?

Choosing outliers in the data as an indicator of a special cause is apt because outliers are data points that significantly differ from the rest of the dataset. These deviations can signal underlying issues, anomalies, or unique events that warrant further investigation. Special causes are often unexpected variations in a process that do not follow the routine variations (common cause) that one would typically expect.

In contrast, averages of multiple observations generally reflect normal variations or trends in data rather than specific, notable events. Trends in time series might indicate systematic behavior but do not necessarily point to unusual instances. Model coefficients tend to be more concerned with the relationships between variables and might not directly highlight special causes without relating them back to residuals or performance metrics that show deviations. Overall, detecting outliers points directly to specific instances that deviate from what is typical, making them strong indicators of special causes in a dataset.