How do outliers affect the mean of a dataset?

Outliers can greatly affect the mean of a dataset because the mean is calculated by summing all the values and dividing by the number of values. When an outlier is present—either significantly higher or lower than the rest of the data—it can distort the overall average. For instance, if a dataset consists of values like 2, 3, 4, and 100, the mean will be heavily influenced by the outlier (100) and will not accurately represent the central tendency of the other values.

This impact is particularly pronounced when the dataset is small, as the outlier can dramatically shift the mean without requiring many additional data points to counterbalance it. In larger datasets, the effect of outliers may still be significant, but it can be mitigated by the presence of more data points that fall within the normal range. Thus, the correct understanding is that outliers can significantly skew the mean, leading to outcomes that may not reflect the actual characteristics of the dataset as a whole.