leftheavy

leftheavy refers to a distribution of data where the tail on the left side of the probability distribution is longer or fatter than the tail on the right side. In simpler terms, it means that extreme low values are more common or more pronounced than extreme high values. This is the opposite of right-heavy, where the right tail is longer.

A left-heavy distribution is characterized by a negative skew. Skewness is a measure of the asymmetry of a probability distribution. A negative skew indicates that the tail on the left side of the distribution is longer than the tail on the right side. The median will typically be greater than the mean in a left-heavy distribution, and the mode will often be the highest point.

Examples of distributions that can be left-heavy include certain financial asset returns, where significant drops in value might occur more frequently or dramatically than significant gains. In other contexts, such as salary distributions, a left-heavy distribution could represent a scenario where most people earn a moderate amount, but a few individuals earn exceptionally low wages. Understanding the skewness of a distribution is crucial for accurate data analysis, statistical modeling, and making informed predictions, as it can significantly impact the interpretation of central tendencies and the likelihood of extreme events.