Outliers can significantly skew measures of center, such as the mean, by pulling the average in their direction, which may not represent the overall data well. For instance, a single extremely high or low value can distort the mean, making it less reflective of the typical values in the dataset. In contrast, the median is more robust against outliers, as it focuses on the middle value, thus providing a more accurate measure of central tendency in such cases. Overall, the presence of outliers necessitates careful consideration when interpreting measures of center.
The most appropriate measures of center for a data set depend on its distribution. If the data is normally distributed, the mean is a suitable measure of center; however, if the data is skewed or contains outliers, the median is more appropriate. For measures of spread, the standard deviation is ideal for normally distributed data, while the interquartile range (IQR) is better for skewed data or when outliers are present, as it focuses on the middle 50% of the data.
an outliers can affect the symmetry of the data because u can still move around it
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 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 most appropriate measures of center for a data set depend on its distribution. If the data is normally distributed, the mean is a suitable measure of center; however, if the data is skewed or contains outliers, the median is more appropriate. For measures of spread, the standard deviation is ideal for normally distributed data, while the interquartile range (IQR) is better for skewed data or when outliers are present, as it focuses on the middle 50% of the data.
an outliers can affect the symmetry of the data because u can still move around it
No. Outliers are part of the data and do not affect them. They will, however, affect statistics based on the data and inferences based on the data.
The box and whisker plot informs you of the 5 number summary, which comprises of the minimum and maximum, the median, and the first and third quartiles. The minumum and maximum give you the range, which is not given by measures of central tendancy. also, if it a modified box and whisker plot, outliers will be marked separatley from the rest of the plot, outliers are also not included in the measures of center.
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 mean is better than the median when there are outliers.
The median is the most appropriate center when the distribution is very skewed or if there are many outliers.
Median, mode, quartiles, quintiles and so on, except when you get to very large number of percentiles.
None - as long as the ouliers move away from the median - which they should.
there are no limits to outliers there are no limits to outliers
When the distribution has outliers. They will skew the mean but will not affect the median.
The mean is most affected. Mode and Median are not influenced as much by outliers.