Each team has an opposite value assumption fueling their argument. Dr. Scott Gottlieb and Peter Huber are arguing that the FDA needs to release medicine earlier by performing less safety testing. They argue that releasing medicine earlier could save the lives of individuals who need the medicine sooner rather than later. Gottlieb and Huber seem to believe that individual safety is more important than public safety. They argued that a certain medication may not work for everyone, but it should be released to the public if it helps anyone at all. On the other side of the debate, Dr. Jerry Avorn and Dr. David Challoner argue that all medicine should be tested thoroughly before being released to the public. They argue that the safety of all people should come before the health of a few people. They value public safety before individual safety. Dr. Avorn and Dr. Challoner believe that all medication should be tested rigorously to ensure the safety of everyone who may use it. Even if the medication works for some people, if it doesn’t work for most people it is unsafe for the public. Both teams of debaters include doctors and professors. Both teams are composed of intelligent and reasonable individuals, but both teams disagree about their value assumptions. To make their conclusion seem reasonable, both teams need to explain why they believe their value priorities are true. However, neither team …show more content…
Jerry Avorn gave some statistics about the FDA’s approval of medication in his opening statement. He discusses how “Forbes” magazine said the FDA approves seventy-seven percent of medication the first time it is submitted. Dr. Avorn also says that article in the "New England Journal of Medicine” said that the FDA often approves medication faster than Europe and Canada. While Dr. Avorn gives the audience the source of his information, he does not give them any more information about the research. The audience doesn’t know the actual numbers. Dr. Avorn gives percentages, but percentages can be deceiving. The percentage could be high, but the actual numbers could be small. The audience also doesn’t know if the people reporting the information have any biases. For example, if the researchers’ salaries are paid by the FDA, the researchers might exaggerate the statistics to make their employer look good. The audience is left unaware of any biases and the actual numbers involved in the research. This information could affect whether the audience wants to believe the