Calculating Conditional Probability for Steroid Use at the Olympics

In summary, the imperfect test at the Olympic games gives positive results for 90% of steroid-users and 2% of non-users. With 5% of all athletes using steroids, and given a negative test result, the probability that the athlete is actually using steroids is around 0.1364.
  • #1
lina29
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Homework Statement


All athletes at the Olympic games are tested for performance-enhancing steroid drug use. The imperfect test gives positive results (indicating drug use) for 90% of all steroid-users but also (and incorrectly) for 2% of those who do not use steroids. Suppose that 5% of all registered athletes use steroids. If an athlete is tested negative, what is the probability that he/she uses steroids?


Homework Equations





The Attempt at a Solution


I did P(defective)=.05
P(Tested defective|defective)=1
P(Tested defective|good)=.02

From there I did P(TD)=(1*.05)+(.02*(1-.05))=.069

(1*.05)/.069=.724 which was wrong. Am I missing a step?
 
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  • #2
lina29 said:

Homework Statement


All athletes at the Olympic games are tested for performance-enhancing steroid drug use. The imperfect test gives positive results (indicating drug use) for 90% of all steroid-users but also (and incorrectly) for 2% of those who do not use steroids. Suppose that 5% of all registered athletes use steroids. If an athlete is tested negative, what is the probability that he/she uses steroids?

Homework Equations


The Attempt at a Solution


I did P(defective)=.05
P(Tested defective|defective)=1
P(Tested defective|good)=.02

From there I did P(TD)=(1*.05)+(.02*(1-.05))=.069

(1*.05)/.069=.724 which was wrong. Am I missing a step?

First of all, sanity check. Doesn't that probability seem a tad high to you? We're talking about the probability that a negative test has mistakenly missed a steroid user. You're saying that almost 3/4 of those testing negative were actually using steroids. Even if the test is imperfect, it's not *that* imperfect. So you should realize your answer is way off.

I would suggest not renaming the categories to "defective" and "good" - it's confusing. I assume that when you wrote "defective", you meant "steroid user". So why is "P(Tested defective|defective)=1"? Shouldn't that be P(tested steroid positive|steroid user) = 0.9?

Also why are you considering the probability of having "tested defective" or tested positive for steroid use? You're asked about the scenario where the athlete tested negative. So all you need to consider are the scenarios that could have given you a negative test (or "tested good" using your label).

I've always found it helpful to draw a probability tree. You might want to do this. Start by having the first branch go to S+ and S- (for steroid user and steroid-nonuser, respectively). Each of those branches to T+ and T- (for steroid test positive and steroid test negative respectively). Now can you write down the probabilities at each branch?

EDIT: Attached an image, can you fill in the probabilities that go into each "?"
 

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What is conditional probability?

Conditional probability is a mathematical concept that measures the likelihood of an event occurring given that another event has already occurred. It is used to analyze the relationship between two events and how the occurrence of one event affects the probability of the other event.

How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the joint occurrence of two events by the probability of the first event. The formula is P(A|B) = P(A and B)/P(B), where P(A|B) is the conditional probability of event A given event B.

What is the difference between marginal and conditional probability?

Marginal probability is the probability of an event occurring without any additional information. It is the overall probability of an event. On the other hand, conditional probability takes into account additional information and measures the probability of an event occurring given that another event has already occurred.

What is the significance of conditional probability in real life?

Conditional probability is used in many real-life applications, such as weather forecasting, medical diagnosis, and risk analysis. It helps in making informed decisions by considering the relationship between different events and their probabilities.

What is Bayes' Theorem and how is it related to conditional probability?

Bayes' Theorem is a mathematical formula that calculates the probability of an event based on prior knowledge or additional information. It is closely related to conditional probability as it uses conditional probability to update the probability of an event as new information becomes available.

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