Which measure of central tendency is most appropriate when data has extreme outliers?

The median is the best measure of central tendency when data has extreme outliers because it represents the middle value, thus minimizing the impact of unusually high or low values. Unlike the mean, which can be skewed significantly by extreme outliers, the median is resistant to such distortions, making it a more accurate reflection of the data’s center in skewed distributions. The robustness of the median is especially useful in fields like finance, where extreme values are common, as it provides a more reliable representation of typical data behavior. The other options are incorrect because: • Option 1 (mean) is sensitive to outliers and may not accurately reflect the data’s central value. • Option 3 (mode) is less informative in datasets without distinct frequency patterns. • Option 4 (standard deviation) measures dispersion, not central tendency. • Option 5 (variance) also measures dispersion and is inappropriate for indicating the dataset's center.