Validating precision – how many measurements do we need?

Abstract

Background: A quantitative analytical method should be sufficiently precise, i.e. the imprecision measured as a standard deviation should be less than the numerical definition of the acceptable standard deviation. We propose that the entire 90% confidence interval for the true standard deviation shall lie below the numerical definition of the acceptable standard deviation in order to assure that the analytical method is sufficiently precise. We also present power function curves to ease the decision on the number of measurements to make. Methods: Computer simulation was used to calculate the probability that the upper limit of the 90% confidence interval for the true standard deviation was equal to or exceeded the acceptable standard deviation. Power function curves were constructed for different scenarios. Results: The probability of failure to assure that the method is sufficiently precise increases with decreasing number of measurements and with increasing standard deviation when the true standard deviation is well below the acceptable standard deviation. For instance, the probability of failure is 42% for a precision experiment of 40 repeated measurements in one analytical run and 7% for 100 repeated measurements, when the true standard deviation is 80% of the acceptable standard deviation. Compared to the CLSI guidelines, validating precision according to the proposed principle is more reliable, but demands considerably more measurements. Conclusions: Using power function curves may help when planning studies to validate precision.