Systematic Error Estimation Based on Grey Relational Analysis

It is known that systematic errors could cause the measurement results deviating from the true values. This could decrease the validity of the measurement. Systematic errors are due to the adverse effects in the measurement methods, apparatus, environments, etc. In measurement practices, due to the complexity of error sources, systematic errors are found difficult to determine or sometimes ignored under the assumption that such errors do not exist. In terms of solutions, simply increasing the number of repeated measurement, which is common in a traditional test, may not be able to reduce or eliminate the systematic error. Based on data distribution, the statistical criteria such as the experimental contrast, the residual error check-up, and the t-distribution inspection are often used to estimate the systematic errors in a measurement. The working principle is based on the statistics principle that the existence of systematic error in a measurement sequence would destroy the quality of randomness. To improve accuracy in a modern test, it is very important to examine the characteristics of the systematic errors and find an effective method to reduce their effects on the measurement.