Please list any reference and no plagiarism. Statistically speaking, we are gene
ID: 3208635 • Letter: P
Question
Please list any reference and no plagiarism. Statistically speaking, we are generally agnostic to which is a bigger problem, type I (false positive) errors or type II (false negative) errors. However, in certain circumstances it may be important to try and put more emphasis on avoiding one or the other. Can you think of an example of where you may want to try harder to avoid one type or another? Can you think of a policy; political, economic, social, or otherwise, that pushes people toward avoiding one type or another? What are the repercussions of such policies?
Explanation / Answer
The short answer to this question is that it really depends upon the situation. In some cases a Type I error is preferable to a Type II error. In other applications a Type I error is more dangerous to make than a Type II error. We need to carefully consider the consequences of both of these kinds of errors, then plan our statistical test procedure accordingly. We will see examples of both situations in what follows.
In most fields of science, Type II errors are not seen to be as problematic as a Type I error. With the Type II error, a chance to reject the null hypothesis was lost, and no conclusion is inferred from a non-rejected null. The Type I error is more serious, because you have wrongly rejected the null hypothesis.
Medicine, however, is one exception; telling a patient that they are free of disease, when they are not, is potentially dangerous.