How to Convert Error to Standard Deviation?

In summary, the conversation discusses how to convert error to standard deviation in a physical measurement. It is explained that the standard deviation has the same dimension as the average and can be calculated using the error and a rule of thumb of 3 standard deviations. However, the certainty of the error must also be taken into account.
  • #1
intervoxel
195
1
How to convert error to standard deviation?

I explain my simple question:

I have a program that requests the standard deviation of a physical measurement.

But I only have the error, let's say v=-3.445643 +- 1.5%. How do I make the conversion?

Please
 
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  • #2
Usually the standard deviation has the same dimension as the average. In your case it would be .015 x 3.445643.
 
  • #3
You don't have enough information to make that statement.

An error only needs a single measurement to apply and says that "the actual value is somewhere in an interval from a-b to a+b".

If, however, you take N measurements, you can talk about standard deviations. If the data are normally distributed (usually a good assumption) then what a standard deviation s is saying is "68.27% of the time, my measurement is within a-s to a+s. 95.45% of the time, my measurement is within a-2s to a+2s. 99.73% of the time, my measurement is within a-3s to a+3s" and so on. These percentages are tabulated and a graphical representation can be seen here: http://en.wikipedia.org/wiki/File:Standard_deviation_diagram.svg

Now if you're not too familiar with where the error is actually coming from, I think a good rule of thumb would be to say your error is equal to 3 standard deviations. In other words, you would be saying that your measurement is within +/- 1.5% 99.73% of the time.

Edit: And like mathman said, your standard deviation should be in the same units as your measurement. You don't want to say your standard deviation is .5%.
 
  • #4
I thank you for the answers.
 
  • #5
Actually let me clarify one point a bit:

An error says "the actual value is somewhere in an interval from a-b to a+b" but it doesn't say with what certainty. That certainty has to exist though but you just don't know it right off the bat. It may be 99% of 99.999%, but it's that certainty that will dictate what the standard deviation is. That's where it goes back to making a lot of measurements.
 
  • #6
Usually when an error is specified it is assumed to be "standard error", which means the error as given by one standard deviation. It all boils down to definition of "error" in the given context.
 

1. What is the difference between error and standard deviation?

Both error and standard deviation are measures of variability in a dataset. However, error refers to the difference between the true value and the observed value, while standard deviation is a measure of the spread of data around the mean. In other words, error is the amount of inaccuracy in a measurement, while standard deviation measures the precision of the data.

2. How is error to standard deviation calculated?

Error to standard deviation is calculated by dividing the error by the standard deviation and multiplying by 100. This gives the percentage of the standard deviation that is accounted for by the error.

3. What does a high error to standard deviation ratio indicate?

A high error to standard deviation ratio indicates that there is a large amount of error present in the data relative to the variability of the data. This can be an indication of inaccurate measurements or inconsistencies in the data.

4. How is error to standard deviation used in statistical analysis?

Error to standard deviation is often used in statistical analysis to assess the reliability and accuracy of data. It can help identify potential sources of error in a study and determine the precision of the data. In some cases, a high error to standard deviation ratio may indicate the need for further investigation or data collection.

5. Can error to standard deviation be used to compare different datasets?

Yes, error to standard deviation can be used to compare different datasets, as it provides a standardized measure of variability. However, it is important to note that datasets with different means and standard deviations may have different error to standard deviation ratios, so it should be used with caution when comparing datasets with different characteristics.

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