Final answer:
The correct answer is option a). The use of the mean in highly skewed distributions is misleading as it can be significantly affected by outliers, pulling it towards the distribution's tail and away from what might be considered the 'central' tendency.
Step-by-step explanation:
The correct answer is option a). Averages, also known as measures of central tendency, can be misleading in certain situations. Specifically, when the mean is used with a highly skewed distribution, it does not provide an accurate center because it is affected by the outliers. The extreme values in a skewed distribution tend to pull the mean towards the tail, making it less representative of the data set's central location.
The median, on the other hand, is a better measure in such cases because it is the middle value of the ordered data and not influenced by outliers. The mode, being the most frequent value, might not be a central tendency measure at all in some distributions, and it does not accurately reflect the skewness of the distribution. In summary, while the mean can serve as a useful measure of central tendency in many cases, its application in highly skewed datasets can be misleading.