I have a dataset, which contains measurments from many different conditions. Since my hypothesis suggested a large difference for the measurements in each condition, in order to clean the data, I analyzed all conditions independently.
I.e. I grouped all measurments from one conditions into quartiles, calculated the interquartile range and then removed any datapoint, which was further than 1.5 times the size of the interquartile range from the median.
Now someone looked over the numbers and remarked, that the rate of removed data was much higher than usual. I re-checked my calculations several times, and arrived at such a high ratio of outliers each time.
Now I was thinking, that if I had analyzed all measurements from each condition together, most likely much less measurements would have been removed, due to the differences between the conditions. However as far as I understand these cleaning methods, they are meant to be used on single (gaußian) distributions, and not the sum of two or more distributions. So which method for data cleaning would actually be the correct one: Cleaning all measurements together or cleaning each condition separately?