The highest values are data values that are very different from specific datasets. These data values do not fall within the overall trend that already exists in the data. The particular values are too low or too high in a particular dataset, which may lead to errors in statistics. For example, if someone measures the length of a child's nose, the total value may be subsidized if Pinocchio is included in a specific class of data values.


It is necessary to examine the data set provided for the analysis of extreme statistical values and how to find extreme values in statistics that may pose certain challenges. Although this can be easily set up using a groundbreaking chart where some values differ from specific data values. So how much volatility has value as external value? We will investigate a specific analysis that provides an external standard for determining the deviation of the data.


What are outliers in statistics?


The definition of extreme values in statistics can be considered as a data department used to describe an unusual range from one point to another. Or we can say that the data are those that are no different from the values specified in the other data set. If someone in a group of teenagers has pinocchio, the length of the 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 seen as very small value and 199 is recognised as very high value. However, extreme values are not always seen as simple values. Let's say someone selected checks before last month:


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


Your average salary is considered $130. However, a lower salary ($20) may be because that person went on vacation; That's why the average weekly wage is $130, which is not a real indicator of their earned income. Their average is more similar to $232 if a limit value ($20) is accepted from the specified dataset. That is why it may not be so easy to look for extraneous values. The data set provided may be similar to:


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


One can guess that 2 are remote and probably 60. But it can be expected that the data set 60 does not exist.


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


The Tukey method, which determines extreme values in statistics, uses an intercellular range to distinguish between very small or very large numbers. This is tantamount to the above method, but it is possible to examine formulas that consist slightly differently and the specifications differ slightly. For example, the Toki method uses the idea of "fences".




Many students find it difficult to find extreme values in statistics, so we mentioned two different ways of calculating them. In addition, there are other advanced ways to get extreme values, such as the Dixon Q test, summarized in ESD, etc.


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