Glad it was helpful!

Also please note that z-score method assumes the data sets has a frequency distribution that are mound shaped and symmetric.

Glad, it helps you in your job. You can also see the outliers using an imported box plot visual powerbi.microsoft.com/en-us/blog/visual-awesomeness-unlocked-box-and-whisker-plots/ Power bi also integrates Python and if you know package such as numpy, matplot lib, you can get a lot of these done visually too

You are welcome

I would think that you can have the mean and standard deviation as calculated measures and then you do your z-score as a new column on which you can use a slicer

Let me first emphasize that there are serval ways in which you can determine if an observation is an outlier. z-score method only applies to data sets with frequency distributions that are mound shaped and symmetric. The z-score tells you how many standard deviations away a value is from the mean. Any z-score greater than 3 or less than -3 is considered to be an outlier. This rule of thumb is based on the empirical rule. From this rule, it is often seen that almost all of the data (99.7%) would be within three standard deviations from the mean. By calculating the z-score you are standardizing the observation, meaning the standard deviation is now 1. Thus, from the empirical rule you expect that 99.7% of the z-scores to be within -3 and 3. Another method you can use to determine the Interquartile Range (IQR) outlier Fences.

@@AnalyzeIt_Nael Amazing explanation, thank you so much!