The correct answer is: C. FDR allows for more false positives.
False discovery rate (FDR) is a measure of the expected proportion of false positives among a set of discoveries. It is often used in multiple testing, where a large number of hypotheses are tested and it is important to control the number of false positives.
FDR is calculated as follows:
$$\text{FDR} = \frac{\text{Number of false positives}}{\text{Number of discoveries}}$$
To calculate FDR, we need to know the number of false positives and the number of discoveries. The number of false positives is the number of hypotheses that are true positives but were incorrectly rejected. The number of discoveries is the number of hypotheses that were rejected, regardless of whether they are true positives or false positives.
FDR is a relatively less conservative measure of error than the false positive rate (FPR). This means that FDR is more likely to reject true positives, in order to control the number of false positives.
A high FDR indicates that a large number of false positives have been accepted as true positives. This can lead to problems, such as wasted resources and incorrect conclusions.
Therefore, the statement “FDR allows for more false positives” is incorrect.