- #1
sfspitfire23
- 2
- 0
Hi guys,
Am hoping to tickle your guys' brains. I have a bunch of sets of data. Some are large (+50) some are quite small (less than 10). I would like a way to separate the elements that cluster closely to the mean of their respective set from the outliers of their set. The idea is to get a sense of what values are "typical" and which are "not typical" in each set. Put another way, which values are like the others and which are not like the others in the set.
I've tried assuming normality and taking all the values that fall within 10% of the mean as the "typical" values. Problem is that the distributions of my small data sets are far from normal.
Anyone have any suggestions? Anyone know a method that might work well?
Thanks
Am hoping to tickle your guys' brains. I have a bunch of sets of data. Some are large (+50) some are quite small (less than 10). I would like a way to separate the elements that cluster closely to the mean of their respective set from the outliers of their set. The idea is to get a sense of what values are "typical" and which are "not typical" in each set. Put another way, which values are like the others and which are not like the others in the set.
I've tried assuming normality and taking all the values that fall within 10% of the mean as the "typical" values. Problem is that the distributions of my small data sets are far from normal.
Anyone have any suggestions? Anyone know a method that might work well?
Thanks