91影视

Hypothesized population proportion $\left(p_0\right)=0.08$

Sample size $\left(n\right)=500$

Level of significance $\left(伪\right)=0.05$

Power = 0.764

When the alternative hypothesis is true, the power is the probability of rejecting the null hypothesis. Increasing the significance threshold from $伪=0.05to伪=0.10$is a good way to start. Because the significance level measures the likelihood of making a type I error, as the significance level rises, the likelihood of making a type I error rises, and the likelihood of making a type II error decreases. As a result, the probability of a type II error reduces the power by one, and the power grows.

When the alternative hypothesis is true, the power is the probability of rejecting the null hypothesis. The sample size was reduced from $500$to $250$

Because the sample size has been reduced, there is less knowledge of the population, resulting in less reliable estimates. Because our estimations are less accurate, we are less likely to reject the null hypothesis correctly (once the alternative hypothesis is true), lowering our power.

When the alternative hypothesis is true, the power is the probability of rejecting the null hypothesis. By changing the true proportion to $p=0.10$instead of $p=0.11$the proportion is closer to the hypothesized proportion of $0.08$ Because the difference between the true and hypothesized proportions is less, detecting that the hypothesized proportion is not the true proportion will be more difficult, and hence the power will be reduced.