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

In summary, the conversation discusses how to calculate a one sigma confidence interval for a Poisson event based on observed occurrence rates. It mentions that for larger numbers, the uncertainty follows the root N rule, but for smaller numbers, the estimate of x is n and the possible 1 sigma confidence intervals are n+-sqrt(n). It also suggests referring to an article by Crow and Gardner for better confidence intervals with a given significance level.
  • #1
theneedtoknow
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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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  • #2
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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Related to How Do You Calculate One Sigma Confidence Intervals for Poisson Events?

1. What is Poisson Distribution?

Poisson Distribution is a statistical probability distribution that describes the number of events that occur within a specific time interval or region of space, given the average rate of occurrence and assuming independence between events.

2. How is Poisson Distribution different from other probability distributions?

Poisson Distribution is unique because it is used to model the probability of rare events occurring, such as natural disasters or machine failures, while other distributions like the normal distribution are used for more common events.

3. What are the key characteristics of Poisson Distribution?

The key characteristics of Poisson Distribution include a discrete set of possible outcomes, a single parameter (lambda) that represents the average rate of occurrence, and the assumption of independence between events.

4. How do you calculate probabilities using Poisson Distribution?

To calculate probabilities using Poisson Distribution, you need to know the average rate of occurrence (lambda) and the specific number of events you are interested in. Then, plug these values into the Poisson probability formula: P(x) = (lambda^x * e^-lambda) / x!, where x is the number of events.

5. What are some real-life applications of Poisson Distribution?

Poisson Distribution is commonly used in fields such as finance, insurance, and economics to model rare events like stock market crashes, natural disasters, and customer arrivals. It is also used in quality control and manufacturing to predict the number of defective products in a batch.

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