3. When measuring the center of the data in a skewed distribution, the median would be a preferred descriptive statistic over the mean for most purposes because: a. the median is the middle value while the mean is most likely b. the mean may be too heavily influenced by outliers and doesn't give a good indication of the center c. the median is less than the mean and smaller numbers are always appropriate for the center d. the mean measures the spread in the data e. the median measures the arithmetic average of the data excluding outliers

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**Understanding Measures of Central Tendency in Skewed Distributions**

**Question:**

When measuring the center of the data in a skewed distribution, the median would be a preferred descriptive statistic over the mean for most purposes because:

a. the median is the middle value while the mean is most likely

b. the mean may be too heavily influenced by outliers and doesn’t give a good indication of the center

c. the median is less than the mean and smaller numbers are always appropriate for the center

d. the mean measures the spread in the data

e. the median measures the arithmetic average of the data excluding outliers

**Explanation:**

In the context of skewed data distributions:

- **Option b** is correct: The mean can be significantly affected by extreme values (outliers), which can give a misleading representation of the central tendency. The median, being the middle value when data are ordered, is not influenced by outliers and thus provides a better indication of the center of the distribution.

**Graphical Depiction:**

While this question does not include a specific graph or diagram, a common representation to understand the mean and median in skewed distributions is through a histogram or a box plot.

- **Histogram:** In a positively skewed distribution (right-skewed), the tail on the right side is longer or fatter than the left side. The mean is typically greater than the median.

- **Box Plot:** Box plots can also be used to visualize skewed distributions. The central box shows the interquartile range, while the line within the box represents the median. Outliers are plotted as individual points outside the whiskers (the lines extending from the box). In skewed data, one whisker will be longer, indicating the direction of the skew.

Understanding these concepts is critical for analyzing data, ensuring that the appropriate measure of central tendency is used to accurately describe the dataset.
Transcribed Image Text:**Understanding Measures of Central Tendency in Skewed Distributions** **Question:** When measuring the center of the data in a skewed distribution, the median would be a preferred descriptive statistic over the mean for most purposes because: a. the median is the middle value while the mean is most likely b. the mean may be too heavily influenced by outliers and doesn’t give a good indication of the center c. the median is less than the mean and smaller numbers are always appropriate for the center d. the mean measures the spread in the data e. the median measures the arithmetic average of the data excluding outliers **Explanation:** In the context of skewed data distributions: - **Option b** is correct: The mean can be significantly affected by extreme values (outliers), which can give a misleading representation of the central tendency. The median, being the middle value when data are ordered, is not influenced by outliers and thus provides a better indication of the center of the distribution. **Graphical Depiction:** While this question does not include a specific graph or diagram, a common representation to understand the mean and median in skewed distributions is through a histogram or a box plot. - **Histogram:** In a positively skewed distribution (right-skewed), the tail on the right side is longer or fatter than the left side. The mean is typically greater than the median. - **Box Plot:** Box plots can also be used to visualize skewed distributions. The central box shows the interquartile range, while the line within the box represents the median. Outliers are plotted as individual points outside the whiskers (the lines extending from the box). In skewed data, one whisker will be longer, indicating the direction of the skew. Understanding these concepts is critical for analyzing data, ensuring that the appropriate measure of central tendency is used to accurately describe the dataset.
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