Skewness in statistics describes the asymmetry from the normal distribution in a set of data. Such a dataset differs from a normal curve which is bell-shaped and perfectly symmetrical. In layman's language, a symmetrical curve can be divided into two equal halves with the mean in the middle. When this is not possible, the curve (and the underlying data) is said to be skewed. A distribution can either be positively or negatively skewed, depending on where there's a higher concentration of data points.

Key Points:

• Skewness measures the asymmetry of a probability distribution about its mean.
• A perfectly symmetrical distribution has skewness of 0.
• Positive skewness (right-skewed) means the tail on the right side is longer or fatter.
• Negative skewness (left-skewed) means the tail on the left side is longer or fatter.
• Option C correctly defines skewness as the degree to which a distribution is nonsymmetric about its mean.
• Option A describes symmetry, not skewness.
• Option B incorrectly references the median rather than the mean.
• Option D describes variance or standard deviation, not skewness.