How can extreme values in data impact trend analysis?

Extreme values, often referred to as outliers, can significantly impact the results of trend analysis, particularly in their effect on statistical measures such as means. When calculating the average of a dataset, the presence of extreme values can skew the results, pulling the average upward or downward depending on whether the outliers are high or low. This bias can lead to misleading conclusions about the data trends.

For instance, in a dataset where most values fall between 10 and 20, if an extreme value of 100 is present, the computed average will be disproportionately elevated by that single outlier. This can obscure the true central tendency of the data and misrepresent the actual trend when making projections or strategic decisions based on the analysis.

Understanding this concept is vital because it underlines the importance of examining data thoroughly, including identifying and addressing outliers, to ensure that trend analysis provides a reliable basis for decision-making.