Results submitted to large-scale quality-control schemes are commonly judged against the mean and standard deviation (SD) of the results from other laboratories. It is desirable to ignore outlying values in estimating this mean and standard deviation, and results more than 2.5 or 3 SD from the mean are commonly rejected. I show that an outlier can so inflate the estimated SD that its presence is not detected by this method. Alternative estimators that are less influenced by outliers are described, and their application to quality-control data is discussed.