Estimating the Population Proportion

Oct 13, 2025 by talynt.org 136 views

Introduction

In a small town of 5,832 people, the mayor is interested in determining the proportion of voters who would support an increase in the food tax. To gather this information, the mayor's assistant surveys 500 randomly chosen people and finds that 240 support the increase. In this article, we will use statistical methods to estimate the proportion of voters who support the food tax increase.

Understanding the Problem

The problem involves estimating a population proportion, which is the proportion of the population that supports the food tax increase. We are given a sample of 500 people, and we know that 240 of them support the increase. Our goal is to use this sample information to make an educated estimate of the population proportion.

Defining the Parameters

Let's define the parameters involved in this problem:

p: The population proportion of voters who support the food tax increase.
n: The sample size, which is 500 in this case.
x: The number of people in the sample who support the food tax increase, which is 240 in this case.

Estimating the Population Proportion

To estimate the population proportion, we can use the sample proportion, which is the proportion of the sample that supports the food tax increase. The sample proportion is calculated as:

p̂ = x/n

where p̂ is the sample proportion, x is the number of people in the sample who support the food tax increase, and n is the sample size.

Plugging in the values, we get:

p̂ = 240/500 = 0.48

This means that 48% of the sample supports the food tax increase.

Calculating the Standard Error

To calculate the standard error of the sample proportion, we can use the following formula:

SE(p̂) = sqrt(p̂(1-p̂)/n)

where SE(p̂) is the standard error of the sample proportion, p̂ is the sample proportion, and n is the sample size.

Plugging in the values, we get:

SE(p̂) = sqrt(0.48(1-0.48)/500) = 0.024

This means that the standard error of the sample proportion is 2.4%.

Constructing a Confidence Interval

To construct a confidence interval for the population proportion, we can use the following formula:

p̂ ± (Z * SE(p̂))

where p̂ is the sample proportion, Z is the Z-score corresponding to the desired confidence level, and SE(p̂) is the standard error of the sample proportion.

For a 95% confidence interval, the Z-score is approximately 1.96. Plugging in the values, we get:

0.48 ± (1.96 * 0.024) = 0.48 ± 0.047

This means that the 95% confidence interval for the population proportion is (0.433, 0.527).

Interpretation

The 95% confidence interval suggests that the true population proportion of voters who support the food tax increase is likely to be between 43.3% and 52.7%. This means that we are 95% confident that the true population proportion lies within this interval.

Conclusion

In this article, we used statistical methods to estimate the proportion of voters who support a food tax increase in a small town. We calculated the sample proportion, standard error, and constructed a 95% confidence interval for the population proportion. The results suggest that the true population proportion of voters who support the food tax increase is likely to be between 43.3% and 52.7%. This information can be useful for the mayor in making informed decisions about the food tax increase.

References

Moore, D. S. (2014). Statistics: Concepts and Controversies. 8th ed. New York: W.H. Freeman and Company.
Rossman, A. J. (2015). Statistics for the Life Sciences. 4th ed. New York: W.H. Freeman and Company.

Appendix

Calculating the Sample Proportion

The sample proportion is calculated as: