Estimating proportions using resampling

  • Thread starter moonman239
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In summary, the conversation discusses a bowling competition with 30 pins and some of them being tabbed. Joe and Bob each roll the ball and hit a certain number of pins, some of which are tabbed. By knowing the total number of pins hit and the number of tabbed pins, they can estimate the number of pins Ann tabbed with 99% confidence. However, they also consider if there is a way to get a more accurate estimate.
  • #1
moonman239
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I just thought of this problem: Suppose Ann holds a bowling competition for Joe and Bob. She has 30 pins. She puts tabs on some pins, but hides them and tells neither Joe nor Bob how many pins have been tabbed. So Joe and Bob decide to make this a fun experiment. Joe steps up, rolls the ball and hits 9 pins, two of which are tabbed. Then Bob rolls the ball and hits 11 pins, three of which are pinned. Two of the non-tabbed pins that Bob hits have also been hit by Joe. Knowing this, how can they go about estimating how many pins Ann tabbed.

My solution: We know that altogether, they hit 18 pins, 5 of which were tabbed. Knowing this, we can be 99% confident that the actual number is 5/18, +/- approx. 19.794239%.

However, I'd still like to know if there's a way to get a better estimate than that.
 
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  • #2
The bowling is essentially drawing without replacement, i.e. from a hypergeometric distribution. With this, the 99% CI is 5 to 15.
 

1. What is resampling and how does it work?

Resampling is a statistical technique used to estimate population parameters, such as proportions, from a sample. It involves repeatedly drawing samples from a given dataset and calculating the desired parameter for each sample. By averaging the results of these calculations, we can obtain an estimate of the population parameter.

2. Why is resampling a useful tool for estimating proportions?

Resampling allows us to generate multiple estimates of a proportion, which can help us to better understand the variability of the estimate. It also does not rely on any assumptions about the distribution of the data, making it a more flexible and robust method compared to traditional approaches.

3. What is the difference between bootstrapping and permutation tests?

Bootstrapping is a type of resampling that involves sampling with replacement from the original dataset. This is useful for estimating proportions because it allows us to create multiple samples that are similar to the original sample. Permutation tests, on the other hand, involve randomly shuffling the data to create new samples. This is useful for testing hypotheses about the difference between two groups.

4. How do I know if my resampling estimate is accurate?

The accuracy of a resampling estimate can be evaluated by calculating the standard error, which measures the variability of the estimate. The lower the standard error, the more accurate the estimate is likely to be. Additionally, comparing the resampling estimate to other traditional estimation methods can also help to assess its accuracy.

5. What are some common applications of resampling for estimating proportions?

Resampling is commonly used in various fields such as medicine, economics, and social sciences to estimate proportions in a population. It can be used to estimate the proportion of patients that will respond to a certain treatment, the proportion of a population living in poverty, or the proportion of voters who support a particular candidate.

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