Nice to hear from another Canadian (Pak C Chan) on this topic. As I see it, the problem with using the terms TE and MU interchangeability is that this implies that both concepts are interchangeable, and this is not the case. It is true that both concepts provide for a way of quantifying the range into which a measured result will be with a specified probability, but the two concepts differ dramatically in how this may be done. Using different terms makes it clear which concept you are using; I believe both concepts have validity depending on the application. The TE concept deals exclusively with Type A error, whether it estimated from a method evaluation study or quality control data. On the other hand, the MU concept includes both Type A and Type B errors, as long as the error can be quantified in some way. This makes the MU concept a powerful tool when the objective is to reduce error in processes by isolating the contributing sources of error. Admittedly, in the examples presented, Type B error was ignored for the sake of simplicity in discussion. As well, the MU concept excludes bias in the estimation of uncertainty, although as PC mentions there are suggestions in the literature about including bias (though I think this is contrary to the what experts advise) in estimating measurement uncertainty. The issue with bias is that bias is unidirectional and can not be propagated in a unidirectional way along with bidirectional error like imprecision, as far as I know. If I am mistaken about this, I would be very interested in hearing from anyone about how to include bias in the propagation of uncertainty. I agree that TEa was originally used to designate a performance standard and was not intended to be a performance characteristic. Nowadays, the term performance standard is preferred for this purpose. However, the way that it was being used by Andy Minett in a previous posting is clearly as a performance characteristic and I must say it is quite an elegant way to characterize potential error (TEa = CV(SDcrit + 1.65) + bias). I still feel that it is important to distinguish between TE and TEa when used in this way, although I accept that it is mostly used to refer to a performance standard. The frustration PC expressed about dealing with bias does not in turn, provide justification for including bias in the estimation of measurement uncertainty either. That is, if it is felt that measured bias does not adequately reflect the “right” bias of reported results, then what would be gained by including bias in the estimation of uncertainty. In addition, if one truly believes that nothing can be done about bias then why bother measuring it in the first place. As a newcomer to this topic, I have no hesitation in declaring my lack of expertise in this area. I welcome all feedback that would correct any misguided thinking on my part. Regards to PC. Cheers David Parry Winnipeg, Canada This email and/or any documents in this transmission is intended for the addressee(s) only and may contain legally privileged or confidential information. Any unauthorized use, disclosure, distribution, copying or dissemination is strictly prohibited. If you receive this transmission in error, please notify the sender immediately and return the original. Ce courriel et tout document dans cette transmission est destiné à la personne ou aux personnes à qui il est adressé. Il peut contenir des informations privilégiées ou confidentielles. Toute utilisation, divulgation, distribution, copie, ou diffusion non autorisée est strictement défendue. Si vous n'êtes pas le destinataire de ce message, veuillez en informer l'expéditeur immédiatement et lui remettre l'original.