In satellite remote sensing, isolating geographical/seasonal/local time regions where global satellite-based data are symmetrically distributed and uni-modal can be difficult and/or tedious. Measurements grouped into a given altitude and latitude and month and local time bin can be driven away from typical behaviour by any number of factors (e.g., the polar vortex, a solar proton event, biomass burning, God's wrath, etc.), thereby altering the “typical” distribution of observed measurements, and hence the probability density function (pdf) of the trace species concentration. The often used method for detecting outliers of employing the MAD (Median Absolute Deviation) does not explicitly make use of a pdf, but, in order for it to be useful, it does make an implicit assumption that the pdf is approximately symmetric about the median value. This talk will discuss the ACE-FTS (Atmospheric Chemistry Experiment – Fourier Transform Spectrometer) instrument and the two-stage procedure for detecting physically unrealistic outliers within the data set for each retrieved species, which is a fixed procedure across all species and does not use the MAD.