Why a Single Outlier Can Skew Your Entire Average
The mean is calculated by summing every value and dividing by the count, so a single extreme value directly shifts that sum by its full amount — with a small dataset especially, one unusually high or low number can pull the mean well away from where most of the data actually clusters, even though every other value is unaffected.
This sensitivity is a mathematical property of the mean itself, not a flaw in your data or your calculation — recognizing it helps you interpret an average correctly.
A concrete illustration
Five values of 10, 12, 11, 13, and 500 average out to over 109 — even though four of the five numbers cluster tightly around 11 or 12. The mean here is technically correct but practically misleading about what's "typical" in this dataset, entirely because of the single value of 500.
What to check when an average looks off
As covered in the mean-vs-median guide, comparing the mean against the median for the same dataset is the fastest way to spot outlier distortion — a median much lower than the mean (in a dataset where most values are on the smaller side) is a strong signal that a few large outliers are inflating the mean.