The new concepts of measurement error's regularities and effect characteristics

TL;DR

This paper challenges traditional measurement error concepts by proposing a new non-classification philosophy, emphasizing error regularities and effects as dependent on cognitive perspectives and artificial conditions, leading to a new uncertainty framework.

Contribution

It introduces a novel measurement theory based on error non-classification, overturning traditional concepts of precision, trueness, and accuracy, and proposes a new uncertainty concept system.

Findings

Error regularities stem from different cognitive perspectives.

Error effect characteristics depend on artificial measurement conditions.

Existing error classification philosophy is fundamentally incorrect.

Abstract

In several literatures, the authors give a new thinking of measurement theory system based on error non-classification philosophy, which completely overthrows the existing measurement concept system of precision, trueness and accuracy. In this paper, by focusing on the issues of error's regularities and effect characteristics, the authors will do a thematic interpretation, and prove that the error's regularities actually come from different cognitive perspectives, are also unable to be used for classifying errors, and that the error's effect characteristics actually depend on artificial condition rules of repeated measurement, and are still unable to be used for classifying errors. Thus, from the perspectives of error's regularities and effect characteristics, the existing error classification philosophy is still incorrect; and an uncertainty concept system, which must be interpreted by the error non-classification philosophy, naturally becomes the only way out of measurement theory.