##
Guidelines for Evaluating and Expressing the
Uncertainty of NIST Measurement Results

## 3. Type A Evaluation of Standard Uncertainty

A Type A evaluation of standard uncertainty may be based on any valid
statistical method for treating data. Examples are calculating the standard
deviation of the mean of a series of independent observations [see
Appendix A, Eq. (A-5)]; using the
method of least squares to fit a curve to data in order to estimate the
parameters of the curve and their standard deviations; and carrying out an
analysis of variance (ANOVA) in order to identify and quantify random effects
in certain kinds of measurements. If the measurement situation is especially
complicated, one should consider obtaining the guidance of a statistician. The
NIST staff can consult and collaborate in the development of statistical
experiment designs, analysis of data, and other aspects of the evaluation of
measurements with the Statistical Engineering Division, Computing and Applied
Mathematics Laboratory. Inasmuch as this Technical Note does not attempt to
give detailed statistical techniques for carrying out Type A evaluations,
references [4-7], and reference
[8] in which a general approach to quality
control of measurement systems is set forth, should be consulted for basic
principles and additional references.