Which of the following best describes the shape of a distribution that is left-skewed?

A left-skewed distribution, also known as negatively skewed, is characterized by a tail that extends further to the left side of the distribution. This means that there are more values concentrated on the higher end of the scale, while the lower end has a smaller frequency of values that can stretch out towards the left. The peak of the distribution is typically located on the right side, where the majority of data points cluster.

In such distributions, the mean is usually less than the median because the extreme low values pull the mean down. This contrasts sharply with a right-skewed distribution, where the tail extends towards the right side. Understanding the shape of distributions is crucial in statistics, as it helps determine the appropriate statistical analyses to use. Thus, identifying the tail on the left side accurately characterizes a left-skewed distribution.