Outlier

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Related to Extreme outlier: Mild outlier

Outlier

A data point significantly different from others in the same set. An outlier is generally due to statistical noise and not to any fundamental difficulty with the data set. Taking the mean and median values of a data set can help reduce the influence of outliers. They are also called outlying observations.
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Mild outliers were defined as viruses that had IC50 values >U and <10 times the median, and extreme outliers as having [IC.
In order to avoid masking and scaling problems (high observations hiding intermediate and smaller populations), extreme outliers and populations with higher concentration levels are identified first.
Another model, containing counts of billed CRVS procedure codes from medical and surgical providers rather than counts of procedural visits, slightly reduced the number of extreme outliers but did not improve the overall [R.
While a lower P/S ratio is generally thought to be better than a higher P/S ratio, the cut off at 3 helps exclude extreme outliers while at the same time giving enough room to ride the momentum of the stocks being found.
In big data, outliers are plentiful and even extreme outliers are not rare.
A data set can have only one mean and its value is influenced by extreme outliers or measurements.
A laboratory with results beyond acceptable limits may choose to remix and reassay the specimens in question or request additional specimens for comparison, Extreme outliers, exceeding predefined parameters, are edited out of the data base because they unduly skew the means.
While a lower P/S ratio is generally thought to be better than a higher P/S ratio, the cut off at 3 helps exclude extreme outliers while, at the same time, gives enough room to ride the momentum of the stocks being found.