Expected Width of the Interval

Oct 13, 2025 by wbrum.org 90 views

Introduction

The Clopper-Pearson interval is a widely used method for constructing confidence intervals for the proportion of a population that possesses a certain characteristic. This method is based on the binomial distribution and provides a conservative estimate of the population proportion. However, the choice of the confidence level and the sample size can significantly affect the width of the interval. In this article, we will explore the relationship between the sample size and the confidence level, specifically focusing on the case where p=1/2.

Background

The Clopper-Pearson interval is a two-sided interval that is constructed by finding the values of p that satisfy the following inequality:

\frac{1}{1 + \frac{(1 - \alpha)}{2n} \cdot \frac{1}{p(1-p)}} \leq p \leq \frac{1}{1 + \frac{(1 - \alpha)}{2n} \cdot \frac{1}{(1-p)p}}

where n is the sample size, α is the confidence level, and p is the population proportion.

The Role of p in the Clopper-Pearson Interval

The value of p plays a crucial role in determining the width of the Clopper-Pearson interval. When p=1/2, the interval is expected to be the widest, as the binomial distribution is symmetric around p=1/2. However, the question remains whether p=1/2 always leads to the worst-case sample size for the Clopper-Pearson interval.

Expected Width of the Interval

The expected width of the interval is given by:

E[W] = \frac{1}{n} \cdot \frac{1}{p(1-p)}

where W is the width of the interval.

When p=1/2, the expected width of the interval is:

E[W] = \frac{1}{n} \cdot \frac{1}{(1/2)(1/2)} = \frac{4}{n}

Worst-Case Width of the Interval

The worst-case width of the interval is given by:

W_{max} = \frac{1}{n} \cdot \frac{1}{p(1-p)} + \frac{1}{n} \cdot \frac{1}{(1-p)p}

When p=1/2, the worst-case width of the interval is:

W_{max} = \frac{4}{n} + \frac{4}{n} = \frac{8}{n}

A Priori Bound on the Number of Samples Needed

To obtain a confidence interval where the expected width and worst-case width are ≤ ε, we need to find an a priori bound on the number of samples needed.

Let's assume that we want to obtain a confidence interval with an expected width of ≤ ε. Then, we can set:

\frac{4}{n} \leq \varepsilon

Solving for n, we get:

n \geq \frac{4}{\varepsilon}

Similarly, if we want to obtain a confidence interval with a worst-case width of ≤ ε, we can set:

\frac{8}{n} \leq \varepsilon

Solving for n, we get:

n \geq \frac{8}{\varepsilon}

Comparison of the Two Bounds

Comparing the two bounds, we can see that the bound on the number of samples needed to obtain a confidence interval with a worst-case width of ≤ ε is always greater than or equal to the bound on the number of samples needed to obtain a confidence interval with an expected width of ≤ ε.

This suggests that p=1/2 does not always lead to the worst-case sample size for the Clopper-Pearson interval.

Conclusion

In conclusion, the value of p plays a crucial role in determining the width of the Clopper-Pearson interval. However, p=1/2 does not always lead to the worst-case sample size for the interval. The expected width and worst-case width of the interval are both affected by the value of p, and the bound on the number of samples needed to obtain a confidence interval with a worst-case width of ≤ ε is always greater than or equal to the bound on the number of samples needed to obtain a confidence interval with an expected width of ≤ ε.

Future Work

Future work could involve exploring the relationship between the sample size and the confidence level for different values of p. Additionally, the development of more efficient methods for constructing confidence intervals for the proportion of a population that possesses a certain characteristic is an area of ongoing research.

References

Clopper, C. J., & Pearson, E. S. (1934). The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika, 26(4), 404-413.
Agresti, A., & Coull, B. A. (1998). Approximate is better than "exact" for interval estimation of binomial proportions. American Statistician, 52(2), 119-126.

Code

The following R code can be used to calculate the expected width and worst-case width of the Clopper-Pearson interval for different values of p and n:

clopper_pearson_interval <- function(p, n, alpha) {
  # Calculate the expected width of the interval
  expected_width <- (1 / n) * (1 / (p * (1 - p)))
worst_case_width <- (1 / n) * (1 / (p * (1 - p))) + (1 / n) * (1 / ((1 - p) * p))
return(list(expected_width = expected_width, worst_case_width = worst_case_width))
}
p <- 0.5
n <- 100
alpha <- 0.05
interval <- clopper_pearson_interval(p, n, alpha)
print(interval)

Q: What is the Clopper-Pearson interval?

A: The Clopper-Pearson interval is a widely used method for constructing confidence intervals for the proportion of a population that possesses a certain characteristic. This method is based on the binomial distribution and provides a conservative estimate of the population proportion.

Q: What is the role of p in the Clopper-Pearson interval?

A: The value of p plays a crucial role in determining the width of the Clopper-Pearson interval. When p=1/2, the interval is expected to be the widest, as the binomial distribution is symmetric around p=1/2.

Q: Does p=1/2 always lead to the worst-case sample size for the Clopper-Pearson interval?

A: No, p=1/2 does not always lead to the worst-case sample size for the Clopper-Pearson interval. The expected width and worst-case width of the interval are both affected by the value of p, and the bound on the number of samples needed to obtain a confidence interval with a worst-case width of ≤ ε is always greater than or equal to the bound on the number of samples needed to obtain a confidence interval with an expected width of ≤ ε.

Q: What is the expected width of the Clopper-Pearson interval?

A: The expected width of the interval is given by:

E[W] = \frac{1}{n} \cdot \frac{1}{p(1-p)}

where W is the width of the interval.

Q: What is the worst-case width of the Clopper-Pearson interval?

A: The worst-case width of the interval is given by:

W_{max} = \frac{1}{n} \cdot \frac{1}{p(1-p)} + \frac{1}{n} \cdot \frac{1}{(1-p)p}

Q: How can I calculate the expected width and worst-case width of the Clopper-Pearson interval?

A: You can use the following R code to calculate the expected width and worst-case width of the Clopper-Pearson interval:

clopper_pearson_interval <- function(p, n, alpha) {
  # Calculate the expected width of the interval
  expected_width <- (1 / n) * (1 / (p * (1 - p)))
worst_case_width <- (1 / n) * (1 / (p * (1 - p))) + (1 / n) * (1 / ((1 - p) * p))
return(list(expected_width = expected_width, worst_case_width = worst_case_width))
}
p <- 0.5
n <- 100
alpha <- 0.05
interval <- clopper_pearson_interval(p, n, alpha)
print(interval)

Q: What is the a priori bound on the number of samples needed to obtain a confidence interval with an expected width of ≤ ε?

A: The a priori bound on the number of samples needed to obtain a confidence interval with an expected width of ≤ ε is given by:

n \geq \frac{4}{\varepsilon}

Q: What is the a priori bound on the number of samples needed to obtain a confidence interval with a worst-case width of ≤ ε?

A: The a priori bound on the number of samples needed to obtain a confidence interval with a worst-case width of ≤ ε is given by:

n \geq \frac{8}{\varepsilon}

Q: Can I use the Clopper-Pearson interval for other types of data?

A: Yes, the Clopper-Pearson interval can be used for other types of data, such as categorical data or count data. However, the method may need to be modified to accommodate the specific characteristics of the data.

Q: Are there any limitations to the Clopper-Pearson interval?

A: Yes, the Clopper-Pearson interval has several limitations. For example, it assumes that the data are binomially distributed, which may not always be the case. Additionally, the method can be computationally intensive for large datasets.

Q: Can I use other methods to construct confidence intervals for proportions?

A: Yes, there are several other methods that can be used to construct confidence intervals for proportions, such as the Wilson interval or the Jeffreys interval. Each method has its own strengths and limitations, and the choice of method will depend on the specific characteristics of the data and the research question being addressed.