Researchers are testing a new diagnostic tool designed to identify a certain condition. The null hypothesis of the significance test is that the diagnostic tool is not effective in detecting the condition. For the researchers, the more consequential error would be that the diagnostic tool is not effective, but the significance test indicated that it is effective 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 I error B) Increase the significance level to decrease the probability of Type I error C) Decrease the significance level to increase the probability of Type I error D) Decrease the significance level to decrease the probability of Type Terror E) Decrease the significance level to decrease the standard error

Option D is correct.

Decrease the significance level to decrease the probability of Type I error.

Step-by-step explanation:

For this question, the null hypothesis is that the new diagnostic tool is not effective in detecting the condition.

The more consequential error described is that the diagnostic tool is not effective, but the significance test indicated that it is effective. This is a type I error.

In Hypothesis testing, a type I error involves rejecting the null hypothesis and accepting the alternative hypothesis when in reality, the null hypothesis is true.

It involves saying there is significant evidence to show that the new diagnostic tool is effective in detecting the condition when in reality, it isn't.

The level of significance (α) of a hypothesis test directly gives the probability of a type I error. Therefore, by setting it lower, we reduce the chances of a type I error.

Hence, to keep the chances of a type I error or more consequential error low, we can do that by reducing the significance level at which the test is performed by obtaining stronger evidence against the null hypothesis H₀ (through a lower p -value) before we can reject the null hypothesis.

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