What can skew the mean of a data set significantly?

The mean of a data set can be significantly skewed by outliers, which are values that lie far outside the range of the majority of the data points. When calculating the mean, each value contributes equally, so a few very high or very low outliers can disproportionately affect the calculated average. For instance, if most of the data consists of values around 10, but one value is 100, this outlier raises the mean significantly above what would be considered a typical value for that data set.

In contrast, clustering refers to the grouping of data points and does not alter the individual values significantly enough to skew the mean, although it can affect the variance. Duplicates can affect measures of frequency but do not inherently skew the mean by providing extreme values. Symmetry in a data distribution indicates that the data is evenly distributed around the mean, which typically results in a stable mean that is not skewed. Thus, outliers are the primary factor that can cause a significant skew in the mean of a data set.