Researcher wants to estimate the proportion of Canadians that are over the age of 65, accurate to within 2% using a 95% confidence interval. What is the minimum number of people that should be sampled?

Formula to find sample size:

n = [(z-value)^2 * p * q]/E^2
... where n = sample size, z-value is 1.96 and is found using a z-table for 95% confidence, p = .5 (when no value is stated), q = 1 - p, ^2 means squared, * means to multiply, and E = .02 (or 2%).

I'll let you take it from here to calculate. Round your answer to the next highest whole number.

To calculate the minimum sample size needed to estimate a proportion with a specified level of precision and confidence, we can use the formula:

n = (Z^2 * p * (1-p))/(E^2)

Here, n represents the desired sample size, Z is the z-value associated with the required confidence level (95% confidence corresponds to a z-value of approximately 1.96), p is an estimate of the proportion being studied, and E is the desired margin of error or precision.

In this case, the researcher wants to estimate the proportion of Canadians over the age of 65 accurate to within 2% (0.02) using a 95% confidence interval. Since no estimate of the proportion (p) is given, we can use 0.5 as a conservative estimate, which maximizes the required sample size.

Plugging in these values:

n = (1.96^2 * 0.5 * (1-0.5))/(0.02^2)

Simplifying the equation:

n = 9604

Therefore, the minimum number of people that should be sampled is 9,604 to estimate the proportion of Canadians over the age of 65 with the desired level of precision and confidence.