Interpreting Uncertainties

In summary, The conversation discusses the concept of error and uncertainty in measurements. The first type of error is related to the limited significant figures displayed by a digital device, while the second type is used when measuring a value with statistical variation over time. These two types are interpreted differently and have a different level of certainty associated with them. It is not always clear which type of error is being used and there is a relationship between the two measures of uncertainty. Additionally, if a quantity is repeatedly measured with the same precision, the standard error would be zero.
  • #1
mikelee8a
2
0
Hi,

This problem has been bugging me for while. I hope someone can explain it.

1) If I measure something with a digital device and I know that the reading is correct, my only uncertainty is the limited significant figures displayed by the device. So if it read 24.6 the actual value couldn't possibly be less the 24.55 or more than 24.65 (assuming device correctly calibrated and rounds is a suitable fashion). So reading written as 24.60(5). The point is that this error bounds the result, with every value in the range being equally likely.

2) Compare with standard error, used when measuring a value which has a statistical variation over time. As far as I can see this is written in exactly the same way as above but is interpreted as meaning there is a 68% chance of the result lying in the range (compared with 100%) with the quoted value being the most probably value.

My questions are, is there a way knowing which type of error is being used? And what is the relationship between these two measures of uncertainty? I know that if many type 1) measurements are made and combined, a normal prob. distribution emerges (central limit theorem I think)
 
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  • #2
You cannot estimate the error of what you cannot measure. If you were to repeatedly measure a quantity and get exactly the same value each time to the same limit of precision, the measured standard error would be zero
 
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1. What is the definition of uncertainty in science?

Uncertainty in science refers to the lack of complete knowledge or understanding about a particular phenomenon or measurement. It is often expressed as a range or margin of error.

2. How do scientists measure uncertainty?

Scientists measure uncertainty by using various statistical methods and mathematical models to estimate the range of possible outcomes or margins of error in their data. This allows them to quantify the level of uncertainty in their findings.

3. Why is it important to consider uncertainty in scientific research?

Considering uncertainty in scientific research is important because it allows for a more accurate and complete understanding of the data. It also helps to account for any potential biases or limitations in the research methods and results.

4. What are the different types of uncertainty in science?

The two main types of uncertainty in science are systematic uncertainty and random uncertainty. Systematic uncertainty refers to errors or biases that consistently affect the data, while random uncertainty refers to random variations in the data that are difficult to predict or control.

5. How can scientists minimize uncertainty in their research?

Scientists can minimize uncertainty in their research by using rigorous and standardized methods, conducting multiple trials or experiments, and using appropriate statistical analyses to account for any potential errors or biases. Collaboration and peer review can also help to reduce uncertainty by providing multiple perspectives and checks on the research findings.

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