Why do this problem?
This problem invites students to work with some tricky conditional probabilities in the context of drug testing in sport. It also introduces the payoff matrix, an important representation in Game Theory.
Introduce the first part of the problem:
"Imagine a drug test that is 99% accurate. That is, if you are drug-free, there's a 99% chance you'll pass the test, and if you have taken the drug, there's a 99% chance you'll fail the test. In addition, imagine we know that 99% of athletes DO NOT take the drug. If an athlete tested and fails the test, what is the probability that they have taken the drug?"
You may wish to show students this animation
to introduce Bayes Theorem together with a visual way of thinking about conditional probability in circumstances such as this. This Plus article
also examines conditional probability in the context of sports doping, including
a tailored version of the animation.
Once students have had a chance to engage with and work on this first question, introduce the second part of the problem. Give students time to make sense of the payoff matrix, and then split the class into two groups, with half the students working on the question "How does the payoff matrix change if they drug test both Dave and Joe?" and the other half working on "How does the payoff matrix
change if they only drug test Dave?"
After students have had time to tackle their question, bring the class back together and invite each group to present their working out and the payoff matrix.
Finally, challenge students to answer the question "How does the payoff matrix change if they randomly drug test either Dave or Joe with a 50% chance?" and then take some time to discuss the pros and cons of different drug-testing regimes.
Imagine Dave takes the drug and Joe doesn't, and they are both tested. What are the different possible outcomes?
What is the probability of each outcome?
Invite students to consider the possible outcomes when three athletes compete for Gold, Silver and Bronze, with different drug testing regimes.
It may be helpful to consider an ordering of the events, with the associated probabilities at each stage.
For example, first the athletes decide whether to take the drug or not. Then the race takes place. Then the drug testing takes place.