Determine uncertainty of collection of data

In summary, the conversation discussed working in a liquid calibration lab and applying for ISO 17025. The process involves verifying customers' flow meters against a standard and recording the data in a calibration report. An example was given of performing four tests at a specific flow rate and calculating the error for each test. The speaker also mentioned using the standard error as a way to report statistical error, but cautioned that with only four data points, there may be a high level of uncertainty. They suggested using online resources to learn more about standard error.
  • #1
Mariob
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Hello, I work in a liquid calibration Lab and we are applying for ISO 17025. Basically we verify customers flow meters against our standard and capture the data onto our calibration report. An example is I would perform 4 tests of a specific flow rate and calculate the error in % for each test. Ex: test1 = .12%, test2 = .08%, test3 = .14% and test4 = .09%. How would I determine the uncertainty based on the given data.
 
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  • #2
You could use the standard error (standard deviation of the mean) which is the 'standard' way of reporting statistical error. However, with only four pieces of data you will have an uncertainty in your uncertainty of roughly 40%. If you are unfamiliar with standard error there are plenty of resources online to help which a google search will turn up.
 

What is uncertainty in data collection?

Uncertainty in data collection refers to the lack of complete certainty or exactness in the measurements or observations made during an experiment or study. It is the potential for error or variation in the data that can affect the accuracy of the results.

Why is it important to determine uncertainty in data collection?

Determining uncertainty in data collection is crucial because it allows scientists to understand the accuracy and reliability of their findings. It also helps to identify potential sources of error and improve the experimental design for future studies.

How is uncertainty calculated in data collection?

Uncertainty in data collection is typically calculated by using statistical methods and formulas. This involves analyzing the range of values in the data, the precision of the instruments used, and the potential for human error.

What are some common sources of uncertainty in data collection?

Some common sources of uncertainty in data collection include measurement errors, instrument limitations, sampling methods, and human error. Environmental factors, such as temperature and humidity, can also contribute to uncertainty in data collection.

How can uncertainty be minimized in data collection?

To minimize uncertainty in data collection, scientists can use precise and accurate instruments, carefully design and plan their experiments, and repeat measurements multiple times to account for any variations. It is also important to properly record and analyze data to identify and eliminate any potential sources of error.

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