What factor can bias the average rate of change in trend analysis?

Extreme values in the data, also known as outliers, can significantly bias the average rate of change in trend analysis. When analyzing data trends, the average is sensitive to extreme values, which can skew the results. For example, if there is a sudden spike or drop in data points due to an unusual event, this can disproportionately affect the calculated average, leading to potentially misleading conclusions about the underlying trend.

In contrast to this, other factors such as the implementation of new methodologies or the incorporation of cyclical factors might change how data is analyzed but do not inherently bias the average rate of change. The elimination of atypical periods may be a method to refine the analysis, yet if done improperly, it could remove influential data that reflects true trends. Therefore, outliers represent a clear influence that can distort the understanding of data trends over time, making them a critical consideration in trend analysis.