Measurement Error vs Uncertainty: Philosophical Differences

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Discussion Overview

The discussion revolves around the distinctions between measurement error and measurement uncertainty, exploring both philosophical and technical aspects. Participants share their thoughts and experiences related to these concepts, particularly in the context of statistical analysis and measurement practices.

Discussion Character

  • Debate/contested
  • Conceptual clarification
  • Technical explanation

Main Points Raised

  • One participant notes the lack of a consistent definition for measurement error and uncertainty, suggesting that the issue may be more philosophical than technical.
  • Another participant proposes that uncertainty is minimized when error terms are binary (-1 or +1), but questions whether uncertainty could be greater when errors are randomly distributed within a small range.
  • A participant states that measurement error can be understood as the mean-actual value, while measurement uncertainty relates to variance.
  • Further clarification is sought regarding whether uncertainty is equivalent to the standard deviation.

Areas of Agreement / Disagreement

Participants express differing views on the definitions and implications of measurement error and uncertainty, indicating that the discussion remains unresolved with multiple competing perspectives.

Contextual Notes

Some participants highlight the ambiguity in terminology and the potential for different interpretations of "uncertainty" in both technical and colloquial contexts.

Watts
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Could some please distinguish the difference between measurement error and measurement uncertainty. I have actually held conversations at conferences over this issue with statisticians from NIST and no one has ever been able to give me a consistent answer. It seems to be more philosophical than anything. I was just wondering if anybody could shed some thoughts on this subject?
 
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See Uncertainty. My "educated guess" is that uncertainty is minimized when, say, all error terms (in the sense of residual = measured value - true value) are either -1 or +1. But error can further be minimized when all error terms are randomly distributed, say, between -0.01 and +0.01, in which case uncertainty may be greater than in the previous (binary) case because eror terms are "all over" (albeit within a tiny interval).
 
The former is mean-actual value and the latter is the variance
 
Thanks

That helps matters. The statement made in the link ("Because of an unfortunate use of terminology in systems analysis discourse, the word "uncertainty" has both a precise technical meaning and its loose natural meaning of an event or situation that is not certain.") provided still leaves things open for discussion. I will study the information provided and the link information further. On a second note it is basically another way of measuring dispersion.
 
balakrishnan_v said:
The former is mean-actual value and the latter is the variance
You are saying that uncertainty is identical to + standard deviation; is that correct?
 

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