Resistant Statistic vs Outlier

A statistic is resistant when extreme values have little effect on it, so it reflects the bulk of the data rather than a few outliers. The median and interquartile range are resistant, while the mean and standard deviation are not. For example, changing the largest value in 2, 4, 6, 8, 10 from 10 to 100 leaves the median at 6 but pulls the mean from 6 up to 24. This is why you prefer resistant measures to summarize skewed data or data with outliers.

An outlier is a point that stands apart from the overall pattern of the data. A common rule flags a value as an outlier when it falls below $Q_1 - 1.5 \times \text{IQR}$ or above $Q_3 + 1.5 \times \text{IQR}$, using the first quartile $Q_1$, the third quartile $Q_3$, and the interquartile range. For example, with $Q_1 = 3$, $Q_3 = 9$, and IQR $= 6$, any value below $3 - 9 = -6$ or above $9 + 9 = 18$ is an outlier. Outliers can be genuine extreme cases or data-entry errors, so you investigate before removing them.