Outliners are when a set of numbers has a number too high or to low. Such as 15 16 17 26 26 is an outliner
The mean is better than the median when there are outliers.
None - as long as the ouliers move away from the median - which they should.
An outlier pulls the mean towards it. It does not affect the median and only affects the mode if the mode is itself the outlier.
median
Outliners are when a set of numbers has a number too high or to low. Such as 15 16 17 26 26 is an outliner
An outlier can increase or decrease the mean and median It usually doesn't affect the mode
The mean is better than the median when there are outliers.
None - as long as the ouliers move away from the median - which they should.
The outlier is capable of affecting mean median mode and range it affects mean because the average has changed if affects median because you have to cross out 1 more letter it doesn't affect mode it does affect range because an outlier is a number that i far away from the other numbers * * * * * It does not affect the median.
When the distribution has outliers. They will skew the mean but will not affect the median.
An outlier pulls the mean towards it. It does not affect the median and only affects the mode if the mode is itself the outlier.
An outlier can significantly impact the median by pulling it towards the extreme value of the outlier, especially when the dataset is small. This can distort the central tendency measure that the median represents and provide a misleading representation of the typical value in the dataset.
the median nerve
it messes up the mean and sometimes the median. * * * * * An outlier cannot mess up the median.
median
No, extremely high or low values will not affect the median. Because the median is the middle number of a series of numbers arranged from low to high, extreme values would only serve as the end markers of the values.