A new drug to treat a certain condition is being tested. The null hypothesis of the test is that the drug is not effective. For the researchers, the more consequential error would be for the drug to be effective, but the test does not detect the effect.

Which of the following should the researchers do to avoid the more consequential error?

a. Increase the significance level to increase the probability of Type II error.
b. Increase the significance level to decrease the probability of Type II error.
c. Decrease the significance level to increase the probability of Type II error.
d. Decrease the significance level to decrease the probability of Type II error.
e. Decrease the significance level to decrease the standard error.

Type I error: We reject the null hypothesis & claim that the drug is effective and in reality, the null hypothesis is correct, with the drug not being effective. This happens when our calculated p-value < significance level

Type II error: We fail to reject the null hypothesis & conclude that the drug truly isn't effective. But in reality, the drug is effective.

This occurs when our calculated p-value > significance level.

So, to correct a type II error, we would be trying to make the p-value lesser than the significance level or make the significance level bigger.

So, increasing the significance level, reduces the probability of a type II error as a higher significance level has a higher chance of being greater than the p-value, thereby reversing a type II error.

Hope this Helps!!!

shristiparmar1221 shristiparmar1221 · Answer 2 · 2021-12-06T10:54:03+01:00

This question is based on probability. Hence,the correct option is (b), i.e. increasing the significance level, reduces the probability of a type II error ..

Given:

The null hypothesis of the test is that the drug is not effective.

For the researchers, the more consequential error would be for the drug to be effective, but the test does not detect the effect.

As per the question,

For hypothesis testing, the two types of error can come up include.The errors are type I and type II errors.

Type I error: We reject the null hypothesis & claim that, the drug is effective and in actual, the null hypothesis is correct, with the drug not being effective.

This happens when our calculated p-value < significance level

Type II error: We have to reject the null hypothesis & conclude that, the drug truly isn't effective. But in reality, the drug is effective.

This occurs when our calculated p-value > significance level.

Therefore, to correct a type II error, we would be make the p-value lesser than the significance level or make the significance level bigger.

Hence,the correct option is (b), i.e. increasing the significance level, reduces the probability of a type II error .

For further details,please prefer this link:

https://brainly.com/question/23044118