How do you interpret the skewness of a data distribution?

Skewness measures the asymmetry of a data distribution around its mean.

When we talk about skewness, we're looking at how much a set of data leans to one side. If a distribution is symmetrical, it means the data is evenly spread out on both sides of the mean. However, if it's not symmetrical, it can be skewed either to the left or the right.

A distribution is **positively skewed** (or right-skewed) if the tail on the right side is longer or fatter than the left side. This means most of the data points are concentrated on the left, with fewer larger values stretching out to the right. For example, if you look at the distribution of incomes in a country, you'll often find that most people earn below the average income, with a few people earning significantly more, creating a right-skewed distribution.

Conversely, a distribution is **negatively skewed** (or left-skewed) if the tail on the left side is longer or fatter than the right side. This indicates that most of the data points are concentrated on the right, with fewer smaller values stretching out to the left. An example of this could be the age at retirement, where most people retire around a certain age, but a few retire much earlier, creating a left-skewed distribution.

Understanding skewness helps in analysing data more accurately, as it gives insight into the direction and extent of the asymmetry. This can be crucial for making predictions and decisions based on the data.