Confidence Intervals for not integers numbers ratio

In summary, the speaker is having trouble calculating confidence intervals for a binomial proportion involving non-integer values. They want to use the Clopper-Pearson method but are unsure of how to theoretically justify the calculations. They are also unsure if there is a way to properly account for weighted events. The speaker is considering using the beta distribution and is looking for advice on how to handle this problem. The other person suggests using the basic approach of calculating efficiency values and taking into account the background subtraction and its uncertainty. They also suggest using conventional continuous methods if there are enough events. Additionally, they ask if the speaker has proper uncertainties for the simulated background sample.
  • #1
fatgianlu
2
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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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  • #2
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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  • #3
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.
 
  • #4
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?
 

1. What is a confidence interval for non-integer numbers ratio?

A confidence interval for non-integer numbers ratio is a range of values that is likely to contain the true ratio between two non-integer numbers. It is calculated using statistical methods and is used to estimate the true value of a population based on a sample.

2. How is a confidence interval for non-integer numbers ratio calculated?

A confidence interval for non-integer numbers ratio is calculated using the sample data, a confidence level (usually 95%), and the standard error of the sample. The formula for calculating a confidence interval for non-integer numbers ratio is (sample ratio ± (critical value x standard error)). The critical value is determined by the confidence level and the sample size.

3. What is the purpose of a confidence interval for non-integer numbers ratio?

The purpose of a confidence interval for non-integer numbers ratio is to provide a range of values that is likely to contain the true ratio between two non-integer numbers. It allows researchers to estimate the true value of a population and determine the level of uncertainty in their findings.

4. What is the difference between a confidence interval for non-integer numbers ratio and a confidence interval for integers ratio?

The main difference between a confidence interval for non-integer numbers ratio and a confidence interval for integers ratio is the type of data being used. A confidence interval for non-integer numbers ratio is used when the data is not whole numbers, while a confidence interval for integers ratio is used when the data is whole numbers. The calculations and interpretation of the results may also differ slightly.

5. How can a confidence interval for non-integer numbers ratio be interpreted?

A confidence interval for non-integer numbers ratio should be interpreted as a range of values that is likely to contain the true ratio between two non-integer numbers. This means that if the same study were repeated multiple times, the true ratio would fall within the calculated confidence interval 95% of the time. The narrower the confidence interval, the more precise the estimate of the true ratio.

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