In a dataset where most values are high and a few low values pull the tail to the left, the distribution is described as

In a dataset where most values are high and a few low values pull the tail to the left, the distribution is described as

• A. Negatively skewed
• B. Positively skewed
• C. Symmetrical
• D. Normal

Answer: (A)

Skewness describes where a distribution’s tail extends. If most values are high and a few very low values pull the tail to the left, the tail is on the left side. This pattern is called negative skewness (left-skewed). In such a distribution, the mean tends to be pulled downward by the low outliers, so it sits below the median, which aligns with the bulk of the data on the high end. The other shapes don’t fit: a tail to the right would be positively skewed, a symmetric distribution has tails that are balanced, and a normal distribution is a specific symmetric bell shape.

Skewness describes where a distribution’s tail extends. If most values are high and a few very low values pull the tail to the left, the tail is on the left side. This pattern is called negative skewness (left-skewed). In such a distribution, the mean tends to be pulled downward by the low outliers, so it sits below the median, which aligns with the bulk of the data on the high end. The other shapes don’t fit: a tail to the right would be positively skewed, a symmetric distribution has tails that are balanced, and a normal distribution is a specific symmetric bell shape.