Which type of statistics are not influenced by outliers?

The correct choice is resistant statistics because these types of statistics are specifically designed to remain stable and reliable despite the presence of outliers in the data set. Resistant statistics include measures like the median and the interquartile range, which do not get skewed by extreme values. This characteristic allows them to provide a more accurate representation of the central tendency or variability when outliers are present, making them crucial in data analysis where outliers can significantly distort results.

In contrast, other statistical measures like the mean and standard deviation can be heavily affected by outliers, leading to potentially misleading conclusions about the data. The mean, for example, is sensitive to extreme values, which can pull it away from the typical range of the majority of the data. Understanding the nature of resistant statistics is vital in data analysis, particularly when dealing with datasets that may contain anomalies or outlier values.