I'm working with data from a resistivity test. However, during the test it is common that a few measurement points are wrong due to technical failure. So I want to find and remove these points.
I have a data size of 1147, most of whos values are very close. When i simply plot it as a scatter plot I can immidetly detect around 10 points that are way of.
I calculated the Z-scores and found 12 points above ABS(3). However, is this a correct way of using the Z score? (since it's not a normal distribution)
I want to calculate the z-scores since it strikes me as a more systematic way to handle the problem, also then i can compare between different resistivity tests and see outliers and their values.
I have also thought about just doing it as a graphical test; plotting the scatter and perhaps a histogram or a boxplot. Any thoughts about that?
So more precisly; Is this a correct way to use the Z-score? Does it seem like a structured/accurate method? Is there any other way that is more commonly used?
Any help is very welcomed! Thank you! /Julia
So I added the data, and the large data points are the ones that are failures.
Tyhe histogram shows a very strong positivly skewde distribution. (also the bin are adjusted so to be able to display it at all.