Confidence Intervals for not integers numbers ratio

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Discussion Overview

The discussion revolves around calculating confidence intervals for a binomial proportion when dealing with non-integer values, particularly in the context of efficiency calculations involving background subtraction from data. Participants explore the applicability of the Clopper-Pearson method and seek theoretical justification for using this method with decimal numbers.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant expresses a need to calculate confidence intervals for a binomial proportion involving non-integer values and seeks theoretical legitimacy for this approach.
  • Another participant requests clarification on the specific problem being addressed, suggesting that understanding the underlying issue may help in providing a solution.
  • A participant explains their context of calculating efficiency from data that includes background noise, which requires the use of non-integer values due to scaling and normalization processes.
  • There is a mention of the Clopper-Pearson method, which is traditionally used for discrete binomial distributions, and a concern about its appropriateness for continuous data.
  • One participant suggests that the main issue may not be the non-integer values but rather the need to account for background subtraction and its uncertainty in the calculations.
  • Another participant proposes that if enough events are available, conventional continuous methods might be applicable despite the discrete nature of the observed events.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the best approach to calculate confidence intervals for non-integer ratios. There are multiple competing views regarding the applicability of the Clopper-Pearson method and the handling of background subtraction.

Contextual Notes

Participants express uncertainty about the theoretical foundations for using confidence intervals with non-integer values and the implications of background subtraction on the calculations. There is also a mention of the beta distribution as a potential area for further exploration.

fatgianlu
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Hi, I’m having a problem with a particular case of binomial proportion.
I want calculate a confidence Intervals for a binomial proportion for an efficiency. This kind of intervals are usually defined for ratios between integers numbers but in my case I had to subtract from both numerators and denominators some decimals numbers. I’d like to use Clopper Pearson method and I’m also able to extract the limits for these decimals numbers but I don’t know how to legitimise this calculation theoretically and if I can. Do you know if there is some way to threat confidence intervals properly using decimals numbers? Or some paper that talks about this issue?
 
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fatgianlu said:
Hi, I’m having a problem with a particular case of binomial proportion.
I want calculate a confidence Intervals for a binomial proportion for an efficiency. This kind of intervals are usually defined for ratios between integers numbers but in my case I had to subtract from both numerators and denominators some decimals numbers. I’d like to use Clopper Pearson method and I’m also able to extract the limits for these decimals numbers but I don’t know how to legitimise this calculation theoretically and if I can. Do you know if there is some way to threat confidence intervals properly using decimals numbers? Or some paper that talks about this issue?
It's not clear to me what you're asking. It might be helpful if you told us the problem you're trying to solve.
 
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Ok,
I'm calculating an efficiency to pass a selection of some data. In this data there is some background that I want to subtract. In order to do so I have a simulated sample of the background. This simualted sample is scaled with several weights. (normalization, efficiency correction, etc..) that leads to have not integers numbers. When i want to calculate the efficency, i.e. ratio between the successes in the trials, I have to cope with not integers numbers. When I want to calculate an uncertainty of a proportion I usually use the Cloipper-Pearson method that is derived form a binomial distribution. The binomial distribution is a discrete probability distribtion and so is not correct in my case where i have not integers but continuous numbers. What I'd like to have is a method to calculate a confidence-level in case of a proportion between continuous numbers. Or much better, a way to take into account a subtraction of weighted event. I read something about using the beta distribution but probably I should improve my stats knowledge... I don't know if the problem is clear.
 
fatgianlu said:
Or much better, a way to take into account a subtraction of weighted event.
That will be necessary, so I don't think the non-integer values are the main issue.
You can still use the basic approach: calculate efficiency values where the probability to observe more/fewer events than observed is below some threshold (2.5% or 5% or whatever). The probability will also have to take the background subtraction and its uncertainty into account.

How many events do you have? If you have enough, you can probably ignore the discrete nature of the observed events, and use conventional continuous methods.

Do you have proper uncertainties for the MC background sample?
 

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