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The mean is the least resistant to outliers because it is influenced by every value in the dataset, including extreme values. In contrast, the median, which represents the middle value, is less affected by outliers, as it depends only on the order of the data. The mode, being the most frequently occurring value, is also generally unaffected by outliers. Thus, in terms of sensitivity to extreme values, the mean is the most vulnerable.
What else can I help you with?
How does the outlier affect the mean and median?
The mean is better than the median when there are outliers.
What is the outlier of the mean median and mode?
The median and mode cannot be outliers. For small samples a mode could be an outlier.
When a data set has an outlier which measures of center best describes?
When a data set has an outlier, the median is often the best measure of center to describe the data. This is because the median is resistant to extreme values and provides a better representation of the central tendency in the presence of outliers. In contrast, the mean can be significantly skewed by outliers, making it less reliable in such cases.
When the mean and median do not coincide?
When the mean and median do not coincide, it typically indicates that the data distribution is skewed. In a positively skewed distribution, the mean is greater than the median, while in a negatively skewed distribution, the mean is less than the median. This discrepancy arises because the mean is sensitive to extreme values, whereas the median is resistant to outliers, making it a better measure of central tendency in skewed distributions. Understanding this difference helps in accurately interpreting the data's characteristics.
When the median and mean are substantially different what does that tell you about the data?
I think it means that our data includes outliers.
In general the median of a data set is more resistant to outliers than the mean.?
Yes, it is.
How does the outlier affect the mean and median?
The mean is better than the median when there are outliers.
Which of the following is least affected if an extreme high outlier is added to your data mean median or standard deviation or ALL?
The median is least affected by an extreme outlier. Mean and standard deviation ARE affected by extreme outliers.
When is the median more useful than the mean?
When the distribution has outliers. They will skew the mean but will not affect the median.
Which descriptive summary measures are considered to be resistant statistics?
Median is a good example of a resistant statistic. It "resists" the pull of outliers. The mean, on the other hand, can change drastically in the presence of an outlier.The interquartile range is a resistant measure of spread.
What is the outlier of the mean median and mode?
The median and mode cannot be outliers. For small samples a mode could be an outlier.
How can you determine which measure of central tendency is best for the set if data?
Mean- If there are no outliers. A really low number or really high number will mess up the mean. Median- If there are outliers. The outliers will not mess up the median. Mode- If the most of one number is centrally located in the data. :)
If there are outliers then the mean will be greater than the median its true or false?
true
When a data set has an outlier which measures of center best describes?
When a data set has an outlier, the median is often the best measure of center to describe the data. This is because the median is resistant to extreme values and provides a better representation of the central tendency in the presence of outliers. In contrast, the mean can be significantly skewed by outliers, making it less reliable in such cases.
Which of the arithmetic mean median and mode are resistant measures of central tendency?
Of these three, the median is most resistant.
When the mean and median do not coincide?
When the mean and median do not coincide, it typically indicates that the data distribution is skewed. In a positively skewed distribution, the mean is greater than the median, while in a negatively skewed distribution, the mean is less than the median. This discrepancy arises because the mean is sensitive to extreme values, whereas the median is resistant to outliers, making it a better measure of central tendency in skewed distributions. Understanding this difference helps in accurately interpreting the data's characteristics.
Why calculate for the mean and median in relation to a sample?
Both the mean and median represent the center of a distribution. Calculating the mean is easier, but may be more affected by outliers or extreme values. The median is more robust.
