What does it mean when a distribution is described as "skewed"?

When a distribution is described as "skewed," it indicates that the data points are not symmetrically distributed around the mean. Instead, they lean or stretch to one side of the mean, resulting in a tail on either the left or right side. This "heaviness" to one side is what characterizes the skewness of the distribution.

In a skewed distribution, the direction of the skewness can be either positive (right skewed) or negative (left skewed). A right-skewed distribution has a longer tail on the right side, where most values are concentrated on the left side, while a left-skewed distribution has a longer tail on the left side, with most values on the right.

This characteristic of skewness is important in statistics as it influences measures of central tendency (mean, median, and mode) and can affect the interpretation of the data. Understanding skewness helps researchers make informed decisions about data analysis methods and the appropriateness of applying certain statistical tests.

The other options describe scenarios that aren’t relevant to the concept of skewness. For instance, a balanced distribution around the mean indicates symmetry, while a perfect bell curve represents a normal distribution, which does not exhibit skewness. Additionally