Abstract
This chapter is an introduction to measurement errors and some related statistics. Error estimation is argued to be an essential part of the measurement process. Error types and error reporting are discussed. Unbiased estimates of sample mean variance and standard deviation statistics are presented as are their standard errors. Serial independence of a time series of measurements is discussed and effective sample size is estimated from the lag 1 autocorrelation within a sample time series. The central limit theorem is presented. Peirce's criterion is discussed as a means of identifying doubtful observations for normally distributed errors. Some examples of combining correlated and uncorrelated errors to obtain the error in a function of measurands are provided. Simple linear least squares fitting of a predictor function to measurement data is examined.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
