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Most Prominent Methods of How to Find Outliers in Statistics

Extreme values are data values that are very different from most specific data sets. These data values fall outside the general trend, which already lies in the data. Extreme values are too low or too high in a particular set of data that can create an error in your statistics. For example, if someone measures the length of a child's nose, their common value may lie if Pinocchio is included in a particular class of data values.


There is a need to examine the data set given to study extreme values in statistics, and how to find extreme values in statistics that may cause some challenges. Although this may be easy to identify with the help of a radical chart in which some values differ from specific data values. So, how much variability does the value as an external value? We'll study a specific analysis that provides an external standard on what develops a data deviation.


What are outliers in statistics?


The definition of extreme values in statistics can be considered as a section of data, which is used to represent an unusual range from point to point. Or we can say that the data is that remains outside the values given other with a set of data. If someone has pinocchio in a group of teenagers, the length of their nose will be considered far away compared to other children.


Examples of outliers in statistics


5, 94, 95, 96, 99, 104, 105, 199


"5" is studied as a very low value while "199" is recognized as a very high value. However, extreme values are not always taken as simple values. Let's say someone before the checks selected last month:


$220, $245, $20, $230.


Your average paycheck is considered as $130. But the smaller paycheck ($20) can be because that person went on holiday; that is why an average weekly paycheck is $130, which is not an actual representation of their earned. Their average is more like $232 if one accepts the outlier ($20) from the given set of data. That is why seeking outliers might not be that simple as it seems. The given data set might resemble as:


60, 9, 31, 18, 21, 28, 35, 13, 48, 2


One can guess that 2 is remote and probably 60. But one would expect that 60 is out in the data set.


How to find the outliers in statistics using the Tukey method?


Tukey's method of detecting extreme values in statistics applies the Interquartile range to separate very small or very large numbers. It is equivalent to the method mentioned above, but one can examine formulas that consist a little differently, and the specifications are slightly different. For example, the Toki method uses the idea of "fences"."


Conclusion


Many students have difficulty in finding extreme values in statistics, so we mentioned two different ways to calculate them. Besides, there are other advanced ways to get the value of extreme values, such as Dixon Q test, generalized ESD, and more. Use the IQR and Tukey method mentioned above to solve extreme value issues.


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