Objectives: This paper examines the pitfalls that arise when an outlier is assessed using a criterion based on a fixed multiple of the standard deviation rather than an established statistical test. Although the former approach is statistically invalid, it is the favored method for identifying outliers in Ontario laboratory quality control protocols.
Design and methods: Computer simulations are used to calculate the probability of a false positive result (classifying a valid observation as an outlier) when outlier criteria based on fixed multiples of the standard deviation are applied to samples containing no outliers.
Results: The estimated probability of a false positive result is tabulated over various sample sizes. Outlier criteria based on fixed multiples of the standard deviation are shown to be highly inefficient.
Conclusions: This work presents arguments for discontinuing the widespread practice of using outlier criteria based on fixed multiples of the standard deviation to identify outliers in univariate samples.