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A political polling agency wants to take a random sample of registered voters and ask whether or not they will vote for a certain candidate. One plan is to select 400 voters, another plan is to select 1,600 voters.Which one of the following is true regarding the standard deviation of the sampling distribution of the sample proportion, p̂, of "yes" responses? The standard deviation of the sampling distribution will be 4 times smaller with sample size 400. The standard deviation of the sampling distribution will be 4 times larger with sample size 400. The standard deviation of the sampling distribution will be 2 times smaller with sample size 400. The standard deviation of the sampling distribution will be 2 times larger with sample size 400. The standard deviation of the sampling distribution will be the same for both sample sizes.

Question

A political polling agency wants to take a random sample of registered voters and ask whether or not they will vote for a certain candidate. One plan is to select 400 voters, another plan is to select 1,600 voters.Which one of the following is true regarding the standard deviation of the sampling distribution of the sample proportion, p̂, of "yes" responses? The standard deviation of the sampling distribution will be 4 times smaller with sample size 400. The standard deviation of the sampling distribution will be 4 times larger with sample size 400. The standard deviation of the sampling distribution will be 2 times smaller with sample size 400. The standard deviation of the sampling distribution will be 2 times larger with sample size 400. The standard deviation of the sampling distribution will be the same for both sample sizes.

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Solution

The standard deviation of the sampling distribution will be 2 times smaller with sample size 1,600.

Here's why: The standard deviation of the sampling distribution of the sample proportion (often called the standard error) is given by the formula:

sqrt[(P(1 - P) / n]

where P is the proportion of "yes" responses in the population, and n is the sample size.

When you quadruple the sample size (from 400 to 1,600), the standard deviation becomes half as large. This is because the sample size n is in the denominator of the formula, so as n increases, the overall value decreases (assuming P remains constant).

So, increasing the sample size leads to a decrease in the standard deviation of the sampling distribution, making the sample proportion a more reliable estimate of the population proportion. This is a key concept in statistics known as the law of large numbers.

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