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Re: statistical fluctuations



Sorry, I read too quickly.


I do think so. To verify use the binomial distribution and compare with
the Poisson. the P. distrib. applies when the probability of occurrence
is very small p << 1. [and ....]

i.e. SD^2 = m(1-p), while for Poisson: SD^2 = m [p is
probability of event occurrence, zero to one; m is mean, i.e. pz
probability of occurrence times number of trials]. Here p is
proportional to the half life X number of nuclei X the efficiency of the
detector. Since I assume the half life is long and the number large
(also satisfies the requirement the number of nuclei doesn't change
during the measurement), I think the P distribution applies. However,
at such a low rate, ten, its value being the mean is rather unlikely.
i.e. the probability of its being 10 or 9 are about the same and less
than 15%, assuming the "actual" value is ten. I'm too lazy to calculate
the values, so I've taken them approximately from Taylor (An Intro. to
Error Analysis) who has a histogram for m = 9.

bc

Ludwik Kowalski wrote:

In a private message Bernard Cleyet asked:

> p.s. why "stat. flucts."?=A0 I think subtracting
> background is more appropriate.

In my illustration one chip recorded 47 tracks
(signal plus background) while another recorded
37 (background). I subtract and obtain signal
equal to 10. But I can not say that signal=3D10
because both 47 and 37 would fluctuate from
chip to chip. The probability that the true signal
is 10 is very very small in this example.

Now consider the Oriani's method. (His numbers
were actually much larger, I made them small to
focus on fluctuations. Suppose that using his
method (one chip) I observe 37 before the
experiment and 47 after the experiment. I conclude
that the signal is 10. Yes, I do not expect that the next
experiment will yield the same signal but the result
will always be positive or zero. Is it correct to say
that the standard deviation for the signal should
be sqrt(10), in this illustration? I am not certain.
Ludwik Kowalski