A data analyst is preparing a report on home sale prices for a specific zip code. After plotting the data, the analyst observes that the distribution is heavily skewed to the right due to a small number of multi-million dollar properties. The goal is to report a single value that represents the typical price for the majority of homes. Which of the following measures should the analyst use to best represent the central tendency of the data?
The correct answer is the median. For a skewed distribution or a dataset with significant outliers, the median is the most appropriate measure of central tendency. The median represents the middle value of the dataset when ordered, and it is not significantly affected by extreme values (outliers).
The mean (or average) is incorrect because it is sensitive to outliers and would be pulled higher by the expensive properties, giving a misleadingly high value for the 'typical' home. The mode is incorrect because it represents the most frequent value, which may not be representative of the central location in a continuous dataset like housing prices, and is best suited for categorical data. Standard deviation is a measure of data dispersion or spread around the mean, not a measure of central tendency.
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Why is the median preferred over the mean in skewed distributions?
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What is the advantage of using the median over the mode for continuous data?
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How do outliers affect the mean and standard deviation in a dataset?
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CompTIA Data+ DA0-002 (V2)
Data Analysis
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