What is the 1.5(IQR) Statistical Outlier Rule?

The 1.5(IQR) Statistical Outlier Rule assumes there are statistical outliers in every column of numerical data values. And, you can use the theory of the box & whisker plot and the 1.5 IQR Rule to find "High Outliers" and "Low Outliers" in your dataset based on the 1.5(IQR) Outlier Rule that is applied to the Interquartile Range (IQR) statistic of one or more columns of data: High Outlier > 75th percentile + 1.5(IQR) Low Outlier < 25th percentile - 1.5(IQR) You can apply the 1.5(IQR) Statistical Outlier Formula to one or more columns of data. Sort the data from the highest to lowest value: identify the 75th percentile which is the value with 75% of the data values below it, and the 25th percentile which has 25% of the data values below it. The 75th percentile - the 25th percentile is the Interquartile Range (IQR) statistic. Then apply the above two IQR formulas above to your calculated IQR statistic to identify the "High Outlier" and "Low Outlier" values in your data. The output below is an example summary report generated by completing the 1.5(IQR) Outlier Analysis Tutorial that applies the 1.5(IQR) Outlier Analysis Rule. "High Outlier" and "Low Outlier" values are unusually high or low values in a column of data that skew descriptive statistics such as Average and have a negative impact on the accuracy and validity of any data analysis.