A disadvantage of the mean as measure of central tendency is that?

Analyze the question: It asks for a disadvantage (weakness) of the arithmetic mean as a measure of central tendency.

Concept: The mean is calculated as:

Mean = (sum of all values) ÷ (number of values)

Because every value in the data set goes into the calculation, the mean is sensitive to unusually large or small values (outliers).

Example: Monthly incomes (in ₦thousands) of 5 workers: 30, 35, 40, 45, 350

• Mean = (30 + 35 + 40 + 45 + 350) ÷ 5 = 500 ÷ 5 = 100
• Median = 40

The mean of 100 is far higher than what most workers earn. The single extreme value (350) pulled it upward and made it unrepresentative.

Checking each option:

• A. Is affected by extreme values: TRUE. This is the main weakness of the mean.
• B. Can only be used for continuous data: FALSE. The mean works for discrete data too (e.g., average number of children per family).
• C. Cannot be used to measure the qualities of a population: This is not the standard textbook disadvantage. The mean applies to quantitative data, but this is not the usual point examiners are testing.
• D. Cannot be determined in group data: FALSE. The mean for grouped data is found using class midpoints: Mean = Σfx ÷ Σf.

Answer: The correct option is A.

Exam tip: Remember the pairing:

• Mean → affected by extreme values (outliers)
• Median → not affected by extremes, good for skewed data such as income
• Mode → can be used for non-numeric data and may have more than one value

If you see "disadvantage of the mean," think "outliers."