Uncertainty in Measurement (Question)

In summary, when measuring a quantity such as the speed of light, if the average value is 110% of the true value and the uncertainty in measurement is less than 10%, it is possible that the true value may be incorrect. This was seen in the example of a PhD thesis where the author's measurement was two standard deviations lower than their advisor's previous measurement, which turned out to be correct. Therefore, the uncertainty in measurement should create a "bubble" around the average measured value that always encloses the true value, assuming the experimental method and theory are correct and all possible errors have been accounted for.
  • #1
BAnders1
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In regards to data analysis, what would it mean if you measured some quantity, say the speed of light for example, and you measured an average value that was 110% of the true value. If your uncertainty in measurement is LESS than 10%, does that mean that something is wrong with your data or experimental method?

In general, should the uncertainty in measurement create a "bubble" around the average measured value that always encloses the true value, given that your experimental method and theory is correct, and that you accounted for every possible error?
 
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  • #2
BAnders1 said:
In regards to data analysis, what would it mean if you measured some quantity, say the speed of light for example, and you measured an average value that was 110% of the true value. If your uncertainty in measurement is LESS than 10%, does that mean that something is wrong with your data or experimental method?

In general, should the uncertainty in measurement create a "bubble" around the average measured value that always encloses the true value, given that your experimental method and theory is correct, and that you accounted for every possible error?
If you believe your data, and your estimate of statistical uncertainty, then the "true value" might be wrong. In my PhD thesis, I used a new and very precise method to measure a parameter that was previously measured by my thesis advisor 10 years earlier. My measurement was two (of his) standard deviations lower, and his was 30 (of my standard deviations) higher. My measurement turned out to be correct. (I got my PhD anyway.)
Bob S
 
  • #3


If the average value measured for the speed of light is 110% of the true value and the uncertainty in measurement is less than 10%, it could indicate that there is some error in the data or experimental method. However, it is also possible that the true value of the speed of light falls within the range of uncertainty, but it is on the higher end of the range.

The uncertainty in measurement should create a "bubble" around the average value that encompasses the true value, given that the experimental method and theory are correct and all possible errors have been accounted for. This is because uncertainty is inherent in any measurement and it is important to acknowledge and account for it in order to have a more accurate and reliable result.

If the uncertainty in measurement is too small, it could mean that errors were not properly identified or accounted for, leading to a potentially inaccurate result. On the other hand, if the uncertainty is too large, it may indicate that the experimental method needs to be refined in order to reduce errors and improve the accuracy of the measurement.

In conclusion, it is important to carefully consider and evaluate the uncertainty in measurement in order to ensure the accuracy and reliability of any data analysis. If the uncertainty is properly accounted for and the experimental method and theory are sound, the "bubble" of uncertainty should always encompass the true value.
 

What is uncertainty in measurement?

Uncertainty in measurement refers to the inherent variability or lack of precision in a measurement. It is the degree to which we are uncertain about the true value of a measured quantity.

Why is uncertainty in measurement important?

Uncertainty in measurement is important because it allows us to understand the limitations of our measurements. It helps us determine the precision and accuracy of our results, and evaluate the reliability and validity of our experiments and data. It also allows us to communicate the reliability of our findings to others.

How is uncertainty in measurement calculated?

Uncertainty in measurement is typically calculated using statistical methods, such as standard deviation or confidence intervals. It involves determining the range of possible values that the true value of a measurement could fall within, based on the precision of the measuring instrument and the number of measurements taken.

What factors contribute to uncertainty in measurement?

There are several factors that can contribute to uncertainty in measurement, including the precision of the measuring instrument, the skill of the person taking the measurement, environmental conditions, and the inherent variability of the quantity being measured. It is important to identify and minimize these sources of uncertainty in order to obtain more accurate and reliable measurements.

How can uncertainty in measurement be reduced?

Uncertainty in measurement can be reduced by using more precise measuring instruments, taking multiple measurements, and ensuring proper calibration and maintenance of equipment. Additionally, careful attention to experimental design and data analysis can help to minimize sources of variability and improve the accuracy of measurements.

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