Why is the $standard deviation$ used more frequently than the $variance$ when describing the spread of a data set?

• A. Because the $standard deviation$ is calculated by squaring the $mean$.
• B. Because the $standard deviation$ is less affected by outliers than the $variance$.
• C. Because the $standard deviation$ is always larger than the $variance$ for any data set.
• D. Because the $standard deviation$ is in the same units as the original data, making interpretation easier.

Answer: (D)

Verified step by step guidance

1. Understand that both variance and standard deviation measure the spread or dispersion of a data set around the mean.
2. Recall that variance is calculated as the average of the squared differences from the mean, which means its units are the square of the original data units (e.g., if data is in meters, variance is in meters squared).
3. Recognize that standard deviation is the square root of the variance, which brings the units back to the same as the original data (e.g., meters), making it easier to interpret in the context of the data.
4. Consider that because standard deviation shares the same units as the data, it provides a more intuitive understanding of variability compared to variance, which can be abstract due to squared units.
5. Conclude that this unit consistency is why standard deviation is more frequently used than variance when describing the spread of a data set.