1. What is the difference between a. and a. The graph shows the simulation of 25 intervals from 25 samples with a 95% confidence level. One interval missed the population parameter (p=0.88). What is the formula for the confidence interval for a sample proportion? The formula for calculating the confidence interval for a sample proportion is: CI = p z*(p(1-p)/n) where p is the sample percentage, z is the z-score for the specified confidence level, and n is the sample size (Gravetter & WaLLnau, 2013).

b) What parameters of the formula above can you modify to ensure that the confidence interval captures the population parameter? To guarantee that the confidence interval includes the population parameter, we can adjust the sample size. Increasing the sample size reduces the standard error, …show more content…
List the conditions necessary for the CLT to be held. Make sure to list alternative conditions for when we know the population distribution is normal vs. when we do not know what the population distribution is; and then when the sample size is barely over 30 vs. when it’s very large. According to the Central Limit Theorem (CLT), the sampling distribution of the sample mean approaches normality as the sample size grows. The criteria required for the CLT to hold are: a. The number of samples is arbitrary and impartial. b. Large sample size (n 30) or typical population distribution. c. The sample size is modest (n 30), and the population distribution is not normal. However, there are no severe outliers. Since the population allocation is regular, the CLT is valid regardless of sample size. Whenever the population allocation is not regular, the number of samples collected must be sufficient for the CLT to hold. If the sample size is a little larger than thirty, the population distribution may be significantly skewed or contain moderate outliers. If the sample size is large, the population distribution may be heavily skewed or contain extreme outliers (Gravetter & Wallnau,