Understanding a Positively Skewed Distribution | CFA® Level 1 Exam

B

In a positively skewed distribution, large values are more common than correspondingly small values. This skews the distribution to the right, moving the mean to the right of the median.

A positively skewed distribution is one in which the tail on the right side of the distribution is longer or "skewed" compared to the left side. This means that there is a larger concentration of data points on the left side of the distribution and a few extreme values on the right side that pull the mean towards the right.

In the context of the provided answers:

A. has fat tails: This answer is correct. A positively skewed distribution often has fat tails, meaning that there are a few extreme values on the right side of the distribution that deviate significantly from the mean. These extreme values contribute to the longer tail on the right side.

B. is skewed to the right: This answer is correct. Positively skewed distributions are also referred to as right-skewed distributions because the tail extends towards the right.

C. has a large variance: This answer is not necessarily true. Skewness is related to the shape of the distribution, while variance measures the spread or dispersion of data points around the mean. Skewness and variance are independent measures, and a positively skewed distribution can have a large or small variance.

D. is skewed to the left: This answer is incorrect. Positively skewed distributions are skewed to the right, not to the left.

In summary, a positively skewed distribution is one that has fat tails and is skewed towards the right side. It does not necessarily imply a large variance.