Hi, all A reasonably well known method of outlier detection in phase data is to convert to frequency and look for outliers more than k multiples of the Median Absolute Deviation. I believe this is how outlier detection is done in Stable32[1]. I have implemented an approximation to this method - I do not convert to "frequency" as such, instead I simply take differences of subsequent phase points. Then calculate the (absolute) median of the result, remove outliers bigger than k multiples of the median, and integrate back to phase. The results agree with Stable32 (bar a factor of 1.48-something on k) - the same number of outliers are identified, so I have a reasonable confidence in my approach. I have a gut feeling that my approach is equivalent to converting to "proper frequency" - but I thought I would ask the more (than me) mathematically gifted members of this lists if I am committing some grave sin in my simplistic approach? Thanks! Ole [1] http://www.stable32.com/Outliers%20in%20Time%20and%20Frequency%20Measurements.pdf