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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
In general the median of a data set is less resistant to outliers than the mean.?
Actually, the median is more resistant to outliers than the mean. The median represents the middle value of a data set when arranged in order, making it less influenced by extreme values. In contrast, the mean is calculated by averaging all values, which can be significantly affected by outliers. Therefore, the median provides a better measure of central tendency when outliers are present.
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.
Which measure of central tendency is robust when outliers are present?
mean
In general the median of a data set is less resistant to outliers than the mean.?
Actually, the median is more resistant to outliers than the mean. The median represents the middle value of a data set when arranged in order, making it less influenced by extreme values. In contrast, the mean is calculated by averaging all values, which can be significantly affected by outliers. Therefore, the median provides a better measure of central tendency when outliers are present.
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.
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..
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.
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.
When is mode the better measure?
The mode is the better measure of central tendency when dealing with categorical data, where we want to identify the most common category. It is also useful in skewed distributions or when there are outliers, as it is not affected by extreme values. Additionally, the mode can be the only measure of central tendency applicable for nominal data, where mean and median cannot be computed.
