How Do You Calculate One Sigma Confidence Intervals for Poisson Events?

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To calculate one sigma confidence intervals for Poisson events, use the formula n ± √n, where n is the number of observed events. This approach assumes an asymptotic normal distribution, which is valid for larger sample sizes. For smaller counts, such as a rate of 5 in 1000, the uncertainty may not follow the same pattern, and alternative methods may be needed. For more precise confidence intervals at a specific significance level, refer to the work of Crow and Gardner. Understanding these calculations is crucial for accurately representing event occurrence rates in data analysis.
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I have been analyzing some data at work, and I have measured the occurrence rates of some event. How do I give a one sigma confidence interval to go along with it, assuming it is a Poisson event? For example, I found that something occurs 20 out of 10 000 times, something else occurs 43 out of 10 000 times, etc. How do I calculate one sigma error bars for these values? I know that for large number of events the uncertainty goes to root N, but what about for smaller numbers like, say, a rate of 5 in a 1000.
 
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Using a sigma confidence interval you are implicitly assuming an asymptotic normal distribution. Or do you want e.g. a 64% error interval?
In the first case, if x is the parameter of the Poisson distribution, then it's variance is x, too. The estimate of x is n, the number of events observed. Then possible 1 sigma confidence intervals are
n+-sqrt(n).
If you want better CIs, with a given significance level (rather than sigma value) p, refer to the article by Crow and Gardner:
http://www.ps.uci.edu/~markm/freq/crowgardner.ps
 
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