Which statistical measures are commonly used with normative data to describe central tendency and spread?

Which statistical measures are commonly used with normative data to describe central tendency and spread?

• A. Mode and standard deviation
• B. Mean and standard deviation; with skewed data, median and interquartile range
• C. Median and variance
• D. Range and skewness

Answer: (B)

Describing a dataset in terms of central tendency and spread is the key idea here. When data are roughly normally distributed, the mean captures the typical value and the standard deviation shows how spread out the values are around that mean, in the same units as the data. This pairing is standard because it gives a clear, interpretable sense of where most values lie and how far they typically deviate from the center. If the data are skewed, the mean can be pulled toward the tail by outliers, which makes it less representative. In that situation, the median serves as a better central value because it isn’t swayed by extreme values, and the interquartile range (the spread of the middle 50% of the data) provides a robust measure of dispersion that isn’t affected by outliers. Other options mix measures that don’t align as well with describing a typical value and its spread. The mode is often not representative and can be unstable, especially for continuous data. Describing spread with the range can be overly sensitive to outliers, and variance is simply the squared unit version of standard deviation, which is less interpretable in the same units as the data. Together, mean with standard deviation suits normal data, while median with interquartile range is better for skewed data.

Describing a dataset in terms of central tendency and spread is the key idea here. When data are roughly normally distributed, the mean captures the typical value and the standard deviation shows how spread out the values are around that mean, in the same units as the data. This pairing is standard because it gives a clear, interpretable sense of where most values lie and how far they typically deviate from the center.

If the data are skewed, the mean can be pulled toward the tail by outliers, which makes it less representative. In that situation, the median serves as a better central value because it isn’t swayed by extreme values, and the interquartile range (the spread of the middle 50% of the data) provides a robust measure of dispersion that isn’t affected by outliers.

Other options mix measures that don’t align as well with describing a typical value and its spread. The mode is often not representative and can be unstable, especially for continuous data. Describing spread with the range can be overly sensitive to outliers, and variance is simply the squared unit version of standard deviation, which is less interpretable in the same units as the data. Together, mean with standard deviation suits normal data, while median with interquartile range is better for skewed data.