Assumptions under rule of huge error?

Hasanthi anuradha Pathberiya @Hasanthi_Pathberiya

08 August 2015 1 560 Report

To use rule of huge error in detecting outliers in a data set is it necessary for the data set to be normally distributed?

http://education.mrsec.wisc.edu/research/topic_guides/outlier_handout.pdf

Peter Samuels

Dear Hasanthi,

I completely disagree with this guide. In my opinion, outliers should never be removed unless there is some evidence to doubt their validity.

If you wish to analyse a data set in a way that this not adversely affected by outliers then my advice would be to use a robust or a nonparametric technique.

I would also suggest that removing outliers in the way suggested in the paper will not guarantee normality in your data set.

I would further suggest that you seem to be approaching this issue the wrong way round: rather than changing your data set to fit the technique, the technique should be chosen to fit your (valid) data set. This means checking your data set for normality before using a parametric test.

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