What is a positive of using the interquartile range (IQR) as a measure of spread?

What is a positive of using the interquartile range (IQR) as a measure of spread?

• A. It is robust to outliers.
• B. It provides the exact mean of the data.
• C. It is heavily influenced by extreme values.
• D. It is only suitable for nominal data.

Answer: (A)

Interquartile range measures how spread out the middle half of the data is, by taking the difference between the 75th and 25th percentiles. Because it depends only on those quartiles, extreme values near the top or bottom don’t pull the measure upward or downward as a mean or standard deviation would. That makes it robust to outliers: you can add a very large or very small value, and the central 50% of the data—and thus the IQR—stays mostly the same. This contrast with measures that use all data points, which get pulled by outliers. In short, for data that are skewed or contain outliers, the IQR gives a stable sense of how spread the core results are, rather than being distorted by the extremes. It also requires numeric, ordered data, not nominal data.

Interquartile range measures how spread out the middle half of the data is, by taking the difference between the 75th and 25th percentiles. Because it depends only on those quartiles, extreme values near the top or bottom don’t pull the measure upward or downward as a mean or standard deviation would. That makes it robust to outliers: you can add a very large or very small value, and the central 50% of the data—and thus the IQR—stays mostly the same. This contrast with measures that use all data points, which get pulled by outliers. In short, for data that are skewed or contain outliers, the IQR gives a stable sense of how spread the core results are, rather than being distorted by the extremes. It also requires numeric, ordered data, not nominal data.