# What does skewed right and skewed left mean?

Skewed right and skewed left are terms used to describe the shape of a distribution or dataset. These terms refer to the direction in which the data points are distributed relative to the peak of the distribution.

When we say a distribution is right-skewed, it means that the tail of the distribution extends more towards the right side of the peak than the left side. In other words, there are more data points with higher values on the right side of the distribution, pulling the mean or average towards the higher end of the scale. The peak or highest point of the distribution is typically located towards the left, with a longer tail stretching towards the right.

To illustrate this concept, let me share a personal experience. Suppose you are a teacher and you are grading your students’ test scores. If the grades are right-skewed, it means that there are a few students who performed exceptionally well, receiving high scores, while the majority of the students have lower scores. In this case, the mean or average grade may be higher than the median, which represents the middle score. The distribution of grades would be longer on the right side of the peak, indicating that more students scored towards the lower end.

On the other hand, when a distribution is left-skewed, it means that the tail of the distribution extends more towards the left side of the peak than the right side. In this case, there are more data points with lower values on the left side of the distribution, pulling the mean or average towards the lower end of the scale. The peak of the distribution is typically located towards the right, with a longer tail stretching towards the left.

Let’s continue with the teaching example to understand left-skewness. If the grades are left-skewed, it means that there are a few students who performed poorly, receiving low scores, while the majority of the students have higher scores. In this scenario, the mean or average grade may be lower than the median, indicating that more students scored towards the higher end. The distribution of grades would be longer on the left side of the peak, suggesting that more students achieved higher scores.

It is important to note that skewness is not limited to grades or test scores but can be observed in various datasets and distributions. For instance, in financial data, the distribution of income might be right-skewed, with a few individuals earning significantly higher incomes than the majority. Similarly, in a dataset representing the lifespan of different species, the distribution might be left-skewed, with most species having relatively longer lifespans and a few species having shorter lifespans.

Skewed right and skewed left refer to the shape of a distribution. Right-skewed distributions have a longer tail on the right side of the peak, indicating more data points with higher values on that side. Left-skewed distributions have a longer tail on the left side of the peak, indicating more data points with lower values on that side. These concepts can be observed in various scenarios, such as grading student test scores, analyzing financial data, or studying lifespan distributions.