No they do not (or at least they have less of a significant impact) and this is the benefit of using the median average over the mean average.
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.
The median is the middle of average of the middle two values from the ordered set of observations. If the extreme values are genuine then they will have no effect on the median. If they are incorrectly measured or recorded data then they may affect the position of the middle of the ordered set of data. However, since there can only be a small number of outliers, their effect on the median will be small.
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.
extreme values don't affect the mode
in general,mean is more stable than median but in the case of extreme values it is better to consider median a stable measure than mean.
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.
The median is the middle of average of the middle two values from the ordered set of observations. If the extreme values are genuine then they will have no effect on the median. If they are incorrectly measured or recorded data then they may affect the position of the middle of the ordered set of data. However, since there can only be a small number of outliers, their effect on the median will be small.
An outlier can significantly affect the median of a data set, although its impact is less pronounced compared to measures like the mean. The median is the middle value when data is arranged in order, so if an outlier is added or removed, it may not change the median unless it is situated among the middle values. For instance, in a data set with an odd number of values, an extreme outlier at one end will not affect the median as long as it does not enter the central position. However, in a smaller data set, the presence of an outlier can shift the median if it changes the arrangement of the middle values.
median
Yes, extreme values, also known as outliers, can significantly affect the mean of a data set. Since the mean is calculated by summing all values and dividing by the number of values, a single extreme value can disproportionately skew the result. This is why the mean may not always be the best measure of central tendency for data sets with outliers; alternatives like the median can provide a more accurate representation of the typical value.
Because it is easily influenced by extreme values (i.e. it is not unbiased).
when there are extreme values in the data
The mean is used to measure the average of a set of values, especially when the data is normally distributed. The median is used to find the middle value of a dataset when there are extreme values or outliers present, as it is less affected by extreme values.
MEDIANUse the median to describe the middle of a set of data that does have an outlier.Advantages:• Extreme values (outliers) do not affect the median as strongly as they do the mean.• Useful when comparing sets of data.• It is unique - there is only one answer.Disadvantages:• Not as popular as mean.
Yes, every dataset with at least one value has a median characteristic, which represents the middle value when the data is ordered. If the dataset has an odd number of values, the median is the middle one, while if it has an even number of values, the median is the average of the two middle values. The median is a useful measure of central tendency, especially in skewed distributions, as it is less affected by extreme values compared to the mean.
You would use the median if the data were very skewed, with extreme values.
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.