Solved – Drop a predefined percentage of outliers

I have a dataset that follows something between a power and exponential law. I'm not happy with the IQR method of detection of outliers because on small sets of data (<50-100), it does not give you an idea of the percentage of outliers that have been droped.
I thought of an iterative method that would drop on each iteration the most outlying number until the the desired percentage is reached. I could run this method only when the IQR method drops more than the desired percentage.
Does it make sense and is there a standard way of dealing with this problem ?

You should have a look at this answer. The advices therein also apply to your problem (detecting outliers in univariate settings when the good part of the data is expected to exhibit a skewed distribution).

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