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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.
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What measure of central tendency is robust when outliers are present?
Meanlol
Which measure of central tendency is robust when outliers are present?
mean
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.
Is the best measure of central tendency always the mean?
Its the one most commonly used but outliers can seriously distort the mean.
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.
What measure of central tendency is robust when outliers are present?
Meanlol
Which measure of central tendency is robust when outliers are present?
mean
When is the mean used as a measure of central tendency?
When there aren't extreme values (outliers)
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.
Is the best measure of central tendency always the mean?
Its the one most commonly used but outliers can seriously distort the mean.
When given a set of data that appears to contain outliers which measure of central tendency is most appropriate to use?
Coefficient of Determination
What is the appropriate measure of central tendency for age?
The appropriate measure of central tendency for age is the median. This is because age is a continuous variable and can have outliers or extreme values, which can skew the mean. The median provides a more robust estimate of the center of the distribution.
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 would be most?
The most appropriate measure of central tendency depends on the nature of the data. The mean is useful for normally distributed data without outliers, while the median is better for skewed distributions or when outliers are present, as it provides a more accurate representation of the central point. The mode is ideal for categorical data where we want to identify the most frequently occurring value. Therefore, the context and characteristics of the data should guide the choice of measure.
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..
When is each measure of central tendency most useful?
Mode: Data are qualitative or categoric. Median: Quantitative data with outliers - particularly if the distribution is skew. Mean: Quantitative data without outliers, or else approx symmetrical.
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. :)
