Which of the following statements is correct concerning statistical sampling in tests of controls?

In the context of hypothesis testing, what is the impact of increasing the sample size on the $p$-value, assuming the effect size remains constant?

If the confidence level is increased from $90\%$ to $99\%$, what happens to the width of the confidence interval for a population mean (assuming all other factors remain constant)?

Suppose a 95% confidence interval for the mean difference in blood pressure between a treatment group and a control group is $(-2,5)$. Based on this interval, what does the confidence interval suggest about the effectiveness of the treatment?

Which of the following is the correct formula for the $n$-period weighted moving average?

Which of the following provides information about the statistical validity of Dr. Lonsbary's study?

Which of the following is a necessary condition for making a valid statistical inference using $confidence intervals$?

For a specific confidence level, what happens to the width of a confidence interval for the mean as the sample size increases (assuming population standard deviation is known)? The confidence interval for the mean is typically given by $\mu \pm z\bullet \frac{\sigma }{\sqrt{n}}$, where $n$ is the sample size.

Assuming a $90\%$ confidence level for a population mean with known standard deviation, the margin of error is approximately equal to the critical value times the standard error. Which of the following best represents the margin of error formula for this scenario?