A histogram that features a long left tail is indicative of a distribution that is negatively skewed, or left-skewed.

In a negatively skewed distribution, the majority of data points are clustered on the right side of the histogram, with fewer points extending toward the left. This results in a “tail” that extends to the left. The peak of the histogram, where the highest bars are situated, represents the mode, which is the value that occurs most frequently within the dataset. In negatively skewed distributions, the mode is typically greater than both the median and the mean.

The mean, median, and mode are essential measures of central tendency that provide insight into the center of the data. In a negatively skewed distribution, the mean is dragged to the left due to the influence of the lower values in the tail, resulting in the mean being less than the median. Similarly, the median, which represents the middle value when the data is arranged in order, is also less than the mode. This relationship among the mean, median, and mode is a defining feature of negatively skewed distributions.

Negatively skewed distributions can be observed in various real-world contexts. For instance, in a scenario where most students achieve high scores on a test but a small number score significantly lower, the resulting distribution of scores would be negatively skewed. Understanding the shape of the distribution is crucial for effectively analyzing the data and making informed decisions based on that analysis.