Box Plot Outlier Analysis Benefits

What do you do with outliers in your dataset that you found using the FREE Outlier and Anomaly Detection Template or another template?

In any dataset, the average, median and mode descriptive statistics for a numerical variable (eg. $) must be the same in order for you to do any statistical analysis on it: regression, Analysis of Variance (ANOVA), t-test, or even simple correlations with other variables. Unusually high or low outlier values in your variable cause the average statistic to be too high or low versus the median and mode descriptive statistics.

Explore the Pivot Outlier Forecasting Analysis Template Report on the Time Series Box Plot Outlier Analysis Examples. Here, you can view an example of a forecasting template and, using the slicer below the available pivot table and chart, you can filter outlier values and see how this impacts a simple forecast.

However, generally, you can choose to treat outliers in your data a few different ways before doing an analysis. You can exclude outlier values in a variable from your analysis to improve the accuracy of forecasts or statistical analysis. Or, you can choose to replace these outlier values in a variable with its median descriptive statistic. But if you use the box & whisker plot to find and visualize outliers, you can replace outlier values with the upper or lower limit (whisker) values that are calculated by using the theory of the box & whisker plot.

There is a great deal of research about outlier detection in datasets, and some of this research is posted in Outlier and Anomaly Detection Research.