There has been some recent discussion about the differences between total error and measurement uncertainty, regarding which is better and which should be used. Rather than rehash the differences, let’s examine some similarities:
1. Both specifications are probability based.
2. Both are models
Being probability based is the bigger problem. If you specify limits for a high percentage of results (say 95% or 99%), then either 5% or 1% of results are unspecified. If all of the unspecified results caused problems this would be a disaster, when one considers how many tests are performed in a lab. There are instances of medical errors due to lab test error but these are (probably?) rare (meaning much less than 5% or 1%). But the point is probability based specifications cannot account for 100% of the results because the limits would include minus infinity to plus infinity.
The fact that both total error and measurement uncertainty are models is only a problem because the models are incorrect. Rather than rehash why, here’s a simple solution to both problems.
Add to the specification (either total error or measurement uncertainty) the requirement that zero results are allowed beyond a set of limits. To clarify, there are two sets of limits, an inner set to contain 95% or 99% of results and an outer set of limits for which no results should exceed.
Without this addition, one cannot claim that meeting either a total error or measurement uncertainty specification will guarantee quality of results, where quality means that the lab result will not lead to a medical error.