Certified Supply Chain Professional (CSCP) Practice Exam 2025 - Free CSCP Practice Questions and Study Guide

Data aggregation typically reduces variance. When data points are aggregated, such as by averaging them or summarizing them into higher-level categories, the extreme values or outliers tend to have less influence on the overall result. This leads to a more stable and consolidated view of the data, thus decreasing variability.

For instance, if you take individual data points from various sources and then calculate their average, the resulting average value is likely to be less spread out than the individual values. Consequently, variability within the aggregated data set is typically lower, meaning that the extremes are averaged out, leading to a narrower range of outcomes.

Moreover, aggregating data can also help highlight central trends, making it clearer to identify patterns that would be obscured by the noise of individual variability. This is critical in supply chain management, where understanding overall performance metrics is essential for decision-making.

In summary, data aggregation smooths out fluctuations, effectively reducing variance and providing a clearer understanding of the data set as a whole.