Statistical outliers are data points that differ significantly from the rest of a dataset. They can be much higher or lower than the other values, making them stand out. For example, if most students in a class score between 70 and 90 on a test, but one student scores 30, that score is considered an outlier. Outliers can occur due to variability in the data or errors in measurement. Identifying outliers is important because they can skew results and affect statistical analyses. In fields like finance or healthcare, recognizing outliers helps professionals make better decisions. By understanding outliers, we can gain deeper insights into the data we are analyzing.