The correct answer is C. A measure of the asymmetry in the distribution of data.
Skewness is a measure of the asymmetry of a probability distribution. A distribution is said to be skewed if it is not symmetrical around its mean. A distribution is said to be positively skewed if the tail on the right side of the distribution is longer than the tail on the left side. A distribution is said to be negatively skewed if the tail on the left side of the distribution is longer than the tail on the right side.
The skewness of a distribution can be calculated using the following formula:
$$\text{Skewness} = \frac{\mu_3}{\sigma^3}$$
where $\mu_3$ is the third moment of the distribution and $\sigma^3$ is the cube of the standard deviation.
The skewness of a distribution can be used to describe the shape of the distribution. A distribution with a skewness of 0 is said to be symmetrical. A distribution with a skewness of greater than 0 is said to be positively skewed. A distribution with a skewness of less than 0 is said to be negatively skewed.
The skewness of a distribution can also be used to identify outliers. Outliers are data points that are significantly different from the rest of the data. Outliers can be identified by looking at the skewness of the distribution. If the skewness of the distribution is large, then there is a good chance that there are outliers in the data.
The skewness of a distribution can also be used to make predictions about the future. For example, if a distribution is positively skewed, then it is likely that the future values of the data will be greater than the mean. If a distribution is negatively skewed, then it is likely that the future values of the data will be less than the mean.
In conclusion, skewness is a measure of the asymmetry of a probability distribution. A distribution is said to be skewed if it is not symmetrical around its mean. A distribution is said to be positively skewed if the tail on the right side of the distribution is longer than the tail on the left side. A distribution is said to be negatively skewed if the tail on the left side of the distribution is longer than the tail on the right side. The skewness of a distribution can be used to describe the shape of the distribution, to identify outliers, and to make predictions about the future.