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Yes, the mean is generally a better measure of central tendency when there are no outliers, as it takes into account all values in the dataset and provides a mathematically precise average. In the absence of outliers, the mean reflects the true center of the data distribution effectively. However, in the presence of outliers, the median might be preferred since it is less affected by extreme values.
What else can I help you with?
Which measure of central tendency is robust when outliers are present?
mean
How does the outlier affect the mean and median?
The mean is better than the median when there are outliers.
Is the best measure of central tendency always the mean?
Its the one most commonly used but outliers can seriously distort the mean.
How you find suspected outlier when you have mean?
Having only the mean is not sufficient to identify outliers. You need some measure of dispersion.
Should the mean not be reported as the primary measure of central tendency when a distribution contains a lot of deviant outcomes?
Yes, the mean should not be reported as the primary measure of central tendency when a distribution contains a lot of deviant outcomes or outliers. This is because the mean can be heavily influenced by extreme values, leading to a distorted representation of the data. Instead, the median is often a better measure in such cases, as it provides a more accurate reflection of the central tendency by being less affected by outliers.
Which measure of central tendency is robust when outliers are present?
mean
How does the outlier affect the mean and median?
The mean is better than the median when there are outliers.
When is the mean used as a measure of central tendency?
When there aren't extreme values (outliers)
How do you find the best measure of central tendency when using outliers?
A weighted mean is probably best. Certainly better than a median which throws away information from most of the observations.
Is the best measure of central tendency always the mean?
Its the one most commonly used but outliers can seriously distort the mean.
How you find suspected outlier when you have mean?
Having only the mean is not sufficient to identify outliers. You need some measure of dispersion.
Why is arithmetic mean considered as the best measure of central tendency?
The arithmatic mean is not a best measure for central tendency.. It is because any outliers in the dataset would affect its value thus it is considered not a robust measure.. The mode or median however would be better to measure central tendency since outliers wont affect it value.. Consider this example : Arithmatic mean dan mode from 1, 5, 5, 9 is 5.. If we add 30 to the dataset then the arithmatic mean will be 10 but the mode will still same.. Mode is more robust than arithmatic mean..
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. :)
Should the mean not be reported as the primary measure of central tendency when a distribution contains a lot of deviant outcomes?
Yes, the mean should not be reported as the primary measure of central tendency when a distribution contains a lot of deviant outcomes or outliers. This is because the mean can be heavily influenced by extreme values, leading to a distorted representation of the data. Instead, the median is often a better measure in such cases, as it provides a more accurate reflection of the central tendency by being less affected by outliers.
If a data set has many outliers which measure of central tendency would be the BEST to use?
In a data set with many outliers, the median is the best measure of central tendency to use. Unlike the mean, which can be significantly affected by extreme values, the median provides a more accurate representation of the central location of the data. It effectively divides the data into two equal halves, making it robust against outliers. Therefore, the median offers a clearer understanding of the typical value in such cases.
Which measure of central tendency is most influenced by outliers?
The mean is the measure of central tendency most influenced by outliers. Since it is calculated by summing all values and dividing by the number of values, extreme values can significantly skew the result. In contrast, the median and mode are less affected by outliers, making them more robust measures in such situations.
What are the uses for median?
Helps you accurately measure the results of a population. It's simply the middle number in a data set, so half of the population is above and half of it is below. It is better than the mean since it is resistant to outliers.
