For a given sample size in hypothesis testing, a. Type II error will not be effected by Type I error. b. the smaller the Type I error, the larger the Type II error will be. c. the smaller the Type I error, the smaller the Type II error will be. d. the sum of Type I and Type II errors must equal to 1.

B.the smaller the Type I error, the larger the Type II error will be

Step-by-step explanation:

In hypothesis, type I error is when we reject the null hypothesis even though it's true while type II error is when we accept the null hypothesis even though it is false.

Now, the type I error is based on the significance level alpha. While the type II error is based on the statistical power beta.

This means that the significance level will affect the statistical power and we know that the significance level is inversely related to the Type II error rate based on beta.

Therefore, we can conclude that;

Having a lower significance level will lead to a decrease in the risk of getting a Type I error, however, it will increase the possibility of having a Type II error.

Thus, option B is correct.

any options?

step-by-step explanation: