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.
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an outliers can affect the symmetry of the data because u can still move around it
Outlier: an observation that is very different from the rest of the data.How does this affect the data: outliers affect data because it means that your calculations might be off which makes it a possibility that more than the outlier is off.
Outliers
Go into your data to determine which values are outliers and if they're significant and random (not an apparent group), eliminate them. This will take them out of your boxplot.
Yes, an observation that is abnormally larger or smaller than the rest of the data can significantly affect the mean, as it will pull the average towards that extreme value. However, the median and mode are less influenced by outliers, as they are not as sensitive to extreme values. The median is the middle value when the data is arranged in order, so outliers have less impact on its value. The mode is the most frequently occurring value, so unless the outlier is the most common value, it will not affect the mode.