Measurement Error vs Uncertainty: Philosophical Differences

In summary, the difference between measurement error and measurement uncertainty is a topic that has sparked philosophical discussions among statisticians. While uncertainty is a precise technical term, it is often used loosely to refer to a lack of certainty in a situation. This can make it difficult to fully understand and distinguish from measurement error, which refers to the difference between a measured value and the true value. Additionally, uncertainty can be minimized by ensuring that error terms are either -1 or +1, while error can be further reduced by having randomly distributed error terms within a small interval. Ultimately, uncertainty can be seen as another way of measuring dispersion, and is often equated to the standard deviation. However, further study and understanding is needed to fully grasp the distinction between these
  • #1
Watts
38
0
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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  • #2
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).
 
  • #3
The former is mean-actual value and the latter is the variance
 
  • #4
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.
 
  • #5
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?
 

1. What is the difference between measurement error and uncertainty?

Measurement error refers to the difference between the measured value and the true value of a quantity. It is caused by limitations of the measurement instrument or human error. Uncertainty, on the other hand, refers to the range of possible values that the true value could fall within. It is a measure of the level of confidence in the measured value.

2. How do measurement error and uncertainty affect scientific experiments?

Measurement error and uncertainty can significantly impact the accuracy and reliability of scientific experiments. If there is a large measurement error, the measured values will deviate greatly from the true values, making the results unreliable. A high level of uncertainty can also make it difficult to draw meaningful conclusions from the data.

3. What are some sources of measurement error and uncertainty?

Measurement error can be caused by various factors, such as limitations of the measuring instrument, environmental conditions, and human error. Uncertainty, on the other hand, can arise from factors such as incomplete knowledge about the quantity being measured, limitations of the measurement technique, and random variations in the measurement process.

4. How can scientists minimize measurement error and uncertainty?

There are several ways to reduce measurement error and uncertainty in scientific experiments. These include using more accurate and precise measurement instruments, controlling environmental variables, and carefully following standardized measurement procedures. Additionally, scientists can use statistical methods to analyze the data and determine the level of uncertainty.

5. Why is it important to distinguish between measurement error and uncertainty?

Distinguishing between measurement error and uncertainty is crucial in scientific research as it allows for a better understanding of the limitations and accuracy of the data. It also helps in identifying potential sources of error and uncertainty, allowing scientists to make necessary adjustments to improve the quality of their measurements. Additionally, accurately reporting and addressing measurement error and uncertainty helps to increase the credibility and reproducibility of scientific findings.

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