Why is the calculated ∆S error 0.0007m instead of 0.0005m?

In summary, the conversation discusses the values of slit width (S) measured by the speaker, with the standard error being plus or minus 0.0005. The speaker's teacher wants them to calculate ∆S error using the formula ∆S = S(subscript 2) - S(subscript 1), with the final answer being 0.0007m. The speaker questions why this is the case and the conversation also touches on analyzing data and obtaining a standard error of 0.0005 using the mean and sample standard deviation.
  • #1
fabsuk
51
0
i have 5 values of slit width S

0.0015m
0.0158m
0.00191m
0.0021m
0.00257m

the standard error is plus or minus 0.0005

however my teacher says she wants us to calculate ∆S error
she says its ∆S=S(subscript 2) - S(subscript 1)

she says the answer is 0.0007m

WHY IS THIS SO?

Fabian
 
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  • #2
If you don't know what the "correct" value is for some quantity,
but are pretty sure you've measured what should be the same thing 5 times, you have to presume that your average value is the "correct" one.

How do you analyze your data (even after fixing #2 = 0.00158)
and obtain a "standard error" of .0005 ?
I got a mean of .001932 and a sample standard deviation of .00038 ...
but the estimate of your error in the "correct" value is the population standard deviation of .00043 .
If you have some reason to think that your variations are normally distributed, you can report the 50%-level rather than the 1-sigma ...
that would get you pretty close to .0005 , I guess.

The only way I can think of to get a .0007
would be to use the two-sigma range (mean - sigma) to (mean + sigma),
with the sample standard deviations. I'd call it unusual.
 

FAQ: Why is the calculated ∆S error 0.0007m instead of 0.0005m?

What is error analysis?

Error analysis is a process used by scientists to identify and analyze the sources of errors in their data and experimental procedures. It involves evaluating the accuracy and precision of measurements, identifying potential sources of bias or variability, and determining the impact of these errors on the overall results.

Why is error analysis important?

Error analysis is important because it helps scientists to ensure the reliability and validity of their data and conclusions. By identifying and addressing sources of error, scientists can improve the accuracy and precision of their measurements and increase the confidence in their findings.

What are the types of errors in error analysis?

The two main types of errors in error analysis are systematic errors and random errors. Systematic errors are consistent and repeatable, and they result in measurements that are consistently over or under the true value. Random errors, on the other hand, are unpredictable and can occur in any direction, resulting in measurements that are scattered around the true value.

How do you calculate the total error in a measurement?

The total error in a measurement is calculated by adding the systematic error and the random error. This can be represented mathematically as: Total error = Systematic error + Random error.

How can you reduce errors in an experiment?

To reduce errors in an experiment, scientists can take several measures such as increasing the sample size, using more precise instruments, controlling variables, and repeating the experiment multiple times. Additionally, careful data collection and analysis, as well as identifying and addressing potential sources of bias, can also help to reduce errors in an experiment.

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