Prior

p-values and Type I Errors are Not the Probabilities We Need

In trying to guard against false conclusions, researchers often attempt to minimize the risk of a “false positive” conclusion. In the field of assessing the efficacy of medical and behavioral treatments for improving subjects’ outcomes, falsely concluding that a treatment is effective when it is not is an important consideration. Nowhere is this more important than in the drug and medical device regulatory environments, because a treatment thought not to work can be given a second chance as better data arrive, but a treatment judged to be effective may be approved for marketing, and if later data show that the treatment was actually not effective (or was only trivially effective) it is difficult to remove the treatment from the market if it is safe.