In statistics, skewness refers to the measure of the asymmetry of the distribution of a data set (simplified).

The skewness can be positive or negative (or may not exist in data set at all).

- A positively skewed distribution will have many low scores and only a few high ones.

- A negative skewed distribution, on the other hand, will have a few low scores and many more high ones.

Example 1: Think about the distribution of annual salary of people living in the US. According to the US Census data, more than one third (around 35%) of people earn $25,000 or less; whereas less than 7% of population earn $100,000. That would be an example of a positively skewed distribution.

Example 2:Consider mastery tests. By definition, mastery test is used to determine whether or not students have accomplished a fundamental skill such as subtraction. A teacher would expect most of his/her students scoring high and only a few getting low scores. So, the distribution of the students' scores on a mastery test would be an example of a negatively skewed distribution.

For this week's discussion, give an example of a skewed distribution (like two examples above). State the direction of its skewedness (positive or negative). And explain why so?