What describes a distribution with one side extending far out?

A distribution characterized by one side extending far out is described as a skewed distribution. In a skewed distribution, the data does not symmetrically cluster around the mean; instead, it has a tail on one side that is longer than the other. This can occur either to the right (positive skew) or to the left (negative skew). For instance, in a positive skew, the majority of the data points cluster on the lower end, with a few higher values stretching the tail to the right.

This understanding is crucial when analyzing data, as it affects the measures of central tendency—mean, median, and mode. In a skewed distribution, the mean typically gets pulled in the direction of the longer tail, which may not accurately represent the center of the data. Awareness of skewness helps in making better decisions regarding data interpretation and statistical analysis.

In contrast, a symmetric distribution has no extended tails and is uniformly shaped around the center, while a uniform distribution has all values appearing with equal frequency. A bimodal distribution features two prominent peaks, indicating two different modes, but it does not necessarily imply the presence of extended tails. Thus, the skewed distribution captures the essence of having one side that extends significantly, distinguishing it from