A Commonly used rule that says that a data point will be considered as an outlier if it has more than 1.5 IQR below the first quartile or above the third quartile . An outlier in a probability distribution function is a number that is more than 1.5 times the length of the data set away from either the lower or upper quartiles. Unfortunately, all analysts will confront outliers and be forced to make decisions about what to do with them. In statistics, Outliers are the two extreme distanced unusual points in the given data sets. A value that "lies outside" (is much smaller or larger than) most of the other values in a set of data. These "too far away" points are called "outliers", because they "lie outside" the range in which we expect them. A simple way to find an outlier is to examine the numbers in the data set. Statistics assumes that your values are clustered around some central value. An outlier is the data point of the given sample or given observation or in a distribution that shall lie outside the overall pattern. An outlier is any value that is numerically distant from most of the other data points in a set of data. Measurement error, experiment error, and chance are common sources of outliers. they are data records that differ dramatically from all others, they distinguish themselves in one or more characteristics. Given the problems they can cause, you might think that it’s best to remove them from your data. Should an outlier be removed from analysis? An outlier is a value that is significantly higher or lower than most of the values in your data. Outlier analysis is a data analysis process that involves identifying abnormal observations in a dataset. If you want to draw meaningful conclusions from data analysis, then this step is a must.Thankfully, outlier analysis is very straightforward. Specifically, if a number is less than ${Q_1  1.5 \times IQR}$ or greater than ${Q_3 + 1.5 \times IQR}$, then it is an outlier. For example, the mean average of a data set might truly reflect your values. This is very useful in finding any flaw or mistake that occurred. Outliers are unusual values in your dataset, and they can distort statistical analyses and violate their assumptions. Depending on the situation and data set, any could be the right or the wrong way. Excel provides a few useful functions to help manage your outliers, so let’s take a look. The number 15 indicates which observation in the dataset is the outlier. In other words, an outlier is a value that escapes normality and can (and probably will) cause anomalies in the results obtained through algorithms and analytical systems. They are the extremely high or extremely low values in the data set. For example in the scores 25,29,3,32,85,33,27,28 both 3 and 85 are "outliers". The extremely high value and extremely low values are the outlier values of a data set. The answer, though seemingly straightforward, isn’t so simple. Outliers are data points that don’t fit the pattern of rest of the numbers. The IQR tells how spread out the "middle" values are; it can also be used to tell when some of the other values are "too far" from the central value. When using Excel to analyze data, outliers can skew the results. There are many strategies for dealing with outliers in data. 5 ways to deal with outliers in data. What are Outliers? The circle is an indication that an outlier is present in the data. SPSS also considers any data value to be an extreme outlier if it lies outside of the following ranges: 3rd quartile + 3*interquartile range; 1st quartile – 3*interquartile range Outlier detection statistics based on two models, the casedeletion model and the meanshift model, are developed in the context of a multivariate linear regression model. 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