A value that "lies outside" (is much smaller or larger than) most of the other values in a set of data. For example in the scores 25,29,3,32,85,33,27,28 both 3 and 85 are "outliers".
Definition of outliers. An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal.
When reviewing a box plot, an outlier is defined as a data point that is located outside the whiskers of the box plot. For example, outside 1.5 times the interquartile range above the upper quartile and below the lower quartile (Q1 - 1.5 * IQR or Q3 + 1.5 * IQR).
An. outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph.
The most effective way to find all of your outliers is by using the interquartile range (IQR). The IQR contains the middle bulk of your data, so outliers can be easily found once you know the IQR.
There are no outliers. Explanation: An observation is an outlier if it falls more than above the upper quartile or more than below the lower quartile.
The interquartile range (IQR) measures the dispersion of the data points between the first and third quartile marks. The general rule for using it to calculate outliers is that a data point is an outlier if it is over 1.5 times the IQR below the first quartile or 1.5 times the IQR above the third quartile.
We say that a data value is considered to be an outlier if the data value is less than q1. Minus 1.5MoreWe say that a data value is considered to be an outlier if the data value is less than q1. Minus 1.5 times the IQR. Or if the data value is greater than q3. Plus 1.5 times the IQR.