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I'm working with distributions of weights that are predominently "normal". The weights on the upper end of the distribution are in error and I'd like to find a method that I can use to automatically "chop" off this portion of the distribution. Based on my inexperienced inspection of these distributions it appears as though using the mean +/- the standard deviation as the range for "good" data and throwing everything else away yields a fairly accurate distribution but I'm not convinced that this is the correct/best way of filtering out the bad data.

I'm not a statistics expert so I'm hoping somebody who is can point me in the right direction.

Thanks