Michael J. answered 01/12/24
Experienced Data Science and Computer Science Tutor
To solve this problem, we use the formula for determining sample size for estimating a population proportion. The formula is:
n = (Z² * p * (1 - p)) / E²
where:
- n is the sample size.
- Z is the Z-score corresponding to the desired confidence level.
- p is the estimated population proportion.
- E is the margin of error.
In this scenario:
- You want a 99% confidence level. The Z-score for 99% confidence is approximately 2.576 (this value comes from a standard Z-score table).
- Since you have no preliminary estimate for the population proportion, a conservative approach is to use p = 0.5. This is because the product p * (1 - p) reaches its maximum at p = 0.5, which gives the largest sample size ensuring coverage for any proportion.
- The desired margin of error (E) is 4%, or 0.04 in decimal form.
Plugging these values into the formula gives:
n = (2.576² * 0.5 * (1 - 0.5)) / 0.04²
Upon calculation, the required sample size (n) is approximately 1037. This means you would need a sample of at least 1037 individuals to be 99% confident that your estimate is within 4% of the true population proportion, assuming no prior knowledge of the population proportion. This size ensures the most conservative estimate, covering all possible scenarios of the true population proportion.