Physics Asked by Antonio Iuliano on September 25, 2021
I do not understand when two Gaussian distributions are considered separated, given the resolution of the variable.
Let us suppose we want to know if the difference between two times of flight is statistically relevant. Kleinknecht’s book suggest a 4$sigma_T$ separation, where $sigma_T$ is the resolution of the apparatus. I have two questions:
Why 4 $sigma_T$? I assume it derives from summing 2$sigma_T$ for each distribution, but in that case, since they are statistical errors, shouldn’t it be $2sqrt{2} sigma_T$? Is the resolution considered a maximum error, even if we can plot the Gaussian distribution?
Assuming that we measure N differences t1-t2. Does this allow us to consider 4 $sigma_T/sqrt{N}$, as the error on the mean? I suppose that not to be true, since the resolution is seen as a ‘limit’ to the separation between two variables. Maybe we refer to the variables t1 and t2, not their means? Then why we compute the difference as the difference between the two peaks?
First item: Statistical relevance is not an absolute but a relative statement. The proper way of stating it is "this is stat. relevant on a $2sigma$ level ($approx 5%$). Therefore, there is nothing wrong in using a $4 sigma$ level. So it would help if you could state the exact sentence and context.
You second question I don't really understand.
Is the resolution considered a maximum error, even if we can plot the Gaussian distribution?
In your example the resolution is the smallest time duration between two packages, which allows to detect them as separated packages. I reckon the Rayleigh criterion could be used in your example. However, there are other definitions of resolution as well.
Assuming that we measure $N$ differences $Delta t = t_1-t_2$. Does this allow us to consider $4 sigma_T / sqrt{N}$, as the error on the mean?
Second item: I guess you are mixing two concepts here.
So, let's assume that $sigma_t = sigma_T$. Then,
Answered by Semoi on September 25, 2021
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