None - as long as the ouliers move away from the median - which they should.
The mean is better than the median when there are outliers.
Outliers are observations that are unusually large or unusually small. There is no universally agreed definition but values smaller than Q1 - 1.5*IQR or larger than Q3 + 1.5IQR are normally considered outliers. Q1 and Q3 are the lower and upper quartiles and Q3-Q1 is the inter quartile range, IQR. Outliers distort the mean but cannot affect the median. If it distorts the median, then most of the data are rubbish and the data set should be examined thoroughly. Outliers will distort measures of dispersion, and higher moments, such as the variance, standard deviation, skewness, kurtosis etc but again, will not affect the IQR except in very extreme conditions.
The median and mode cannot be outliers. For small samples a mode could be an outlier.
an outliers can affect the symmetry of the data because u can still move around it
It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.
The mean is better than the median when there are outliers.
When the distribution has outliers. They will skew the mean but will not affect the median.
It is not.
Outliers are observations that are unusually large or unusually small. There is no universally agreed definition but values smaller than Q1 - 1.5*IQR or larger than Q3 + 1.5IQR are normally considered outliers. Q1 and Q3 are the lower and upper quartiles and Q3-Q1 is the inter quartile range, IQR. Outliers distort the mean but cannot affect the median. If it distorts the median, then most of the data are rubbish and the data set should be examined thoroughly. Outliers will distort measures of dispersion, and higher moments, such as the variance, standard deviation, skewness, kurtosis etc but again, will not affect the IQR except in very extreme conditions.
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.
The median and mode cannot be outliers. For small samples a mode could be an outlier.
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. :)
The median is a more robust measure than the average, which means it is more resilient to the effects of outliers in your dataset.
The median is the most appropriate center when the distribution is very skewed or if there are many outliers.
an outliers can affect the symmetry of the data because u can still move around it
It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.It shows the minimum, lower quartile, median, upper quartile and maximum of a set of observations. It may show outliers separately.
true