Of the ten statements on hypothesis testing logic below, four are true. Which are true?

- Standard error is a measure of statistical accuracy. A smaller standard error leads to more confidence in the precision of your sample mean

- If the null hypothesis is false, then the probability of a Type I error is 0, even if power is less than 1

- Increasing the critical alpha decreases Type II error rates as it becomes easier to reject the null

- A study with 80% power has an 85% chance of rejecting the null if you include the 5% Type I error rate

- Increasing power increases Type I error rates as the chance of rejecting the null increases

If your power is 1 it means the null is definitely false as there's a 100% chance of rejecting the null

Your p value is the probability that the null is true, which is why we reject it at p < .05

- A p value of exactly 0 would mean we can actually accept the alternative as there is a zero probability that the null is true

- Increasing the critical alpha increases both power and Type I error rates

- High statistical power when you fail to reject the null is evidence that you made a Type II error