Olympic Drug Testing: Probability Analysis

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SUMMARY

The discussion focuses on the probability analysis of Olympic drug testing, specifically addressing the likelihood of an athlete using steroids based on test results. Key probabilities established include P(S) = 0.02 for steroid use, P(T|S) = 0.95 for a positive test given drug use, and P(T|~S) = 0.2 for a positive test given no drug use. The application of Bayes' Theorem is emphasized for calculating the probability of drug use given a positive test result, denoted as P(S|T). Participants are encouraged to convert the problem description into mathematical symbols for clarity and accuracy.

PREREQUISITES
  • Understanding of Bayes' Theorem
  • Basic probability concepts (conditional probability, independent events)
  • Familiarity with statistical terminology (e.g., P(S), P(T|S))
  • Ability to interpret Venn diagrams in probability contexts
NEXT STEPS
  • Study the application of Bayes' Theorem in real-world scenarios
  • Learn about conditional probability and its implications in testing
  • Explore statistical tools for probability analysis, such as R or Python libraries
  • Investigate the impact of false positives and false negatives in drug testing
USEFUL FOR

Statisticians, data analysts, sports scientists, and anyone involved in drug testing protocols or probability analysis in competitive sports.

rohan.pitt
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Guys I am stuck with this can anyone help me??/

Suppose that 2% of all Olympic athletes use steroids or other performance-enhancing drugs. Because drug
use is a serious violation of Olympic rules, the International Olympic Committee has implemented random
drug tests. Let us assume that the test will report positive with probability 0.95 if the athlete uses drugs,
and with probability 0.2 if the athlete does not use drugs.
a) A randomly selected athlete has a positive drug test. What is the probability that he uses drugs?
b) A randomly selected athlete has a negative drug test. What is the probability that she uses drugs?
c) In fact, an athlete is typically asked to give two samples, A and B, which are tested independently.
Assuming that the results of the A and B tests truly are independent, and that both of an athlete’s samples
are positive, what is the probability that he uses drugs?
d) Given that an athlete’s A test is positive, what is the probability that her B test will also be posi-
tive?
 
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ok I am seriously not able to do much here but going to give what all I could do

P(S)= prob of using steroids = 0.2
P(T|S) = Prob of testing positive given the athlete takes steroids
P(T|~S) = Prob of testing positive given the athlete doen not takes steroids

1) we need to find (T and S) = p(T|S)/p(S) = 0.019

2) we need to find (~T and S) = p(~T|S)/p(S) =??

3) ??
4) ??

Please help me understand this
 
Simon Bridge said:
Welcome to PF;
To get the most out of these forums, please show us your best previous attempt. This helps us target our response to your individual need.

Hey above is my solution not able to get it hence please help
 
Last edited:
rohan.pitt said:
ok I am seriously not able to do much here but going to give what all I could do

P(S)= prob of using steroids = 0.2
P(T|S) = Prob of testing positive given the athlete takes steroids
P(T|~S) = Prob of testing positive given the athlete doen not takes steroids

1) we need to find (T and S) = p(T|S)/p(S) = 0.019

2) we need to find (~T and S) = p(~T|S)/p(S) =??

3) ??
4) ??

Please help me understand this

Hey rohan.pitt and welcome to the forums.

Hint: What does P(T and S) + P(~T and S) equal to? (Think about what (T and S) OR (~T and S) in terms of sets and Venn diagrams)
 
rohan.pitt said:
Hey above is my solution not able to get it hence please help
Sorry I could not respond right away - I have stuff to do like sleep, go to work, answer other peoples questions and so on ;) I'd had to wait 14 hours before you got back to me as it is ... looking at the time-stamp I was having breakfast with a beautiful woman about then - I'm sure you understand.
 
Last edited:
rohan.pitt said:
ok I am seriously not able to do much here but going to give what all I could do

P(S)= prob of using steroids = 0.2
P(T|S) = Prob of testing positive given the athlete takes steroids
P(T|~S) = Prob of testing positive given the athlete doen not takes steroids
well P(S) is incorrect and you left off some values that you have been given:
according to the description in post #1
##P(S)=0.02##
##P(T|S)=0.95##
##P(T|\neg S) = 0.2##

The majority of this question is about how you turn the written description into the mathematical symbols ... so "the probability that a positive test means the athlete takes steroids" is written P(S|T) ... so how would you write "the probability that a negative test means the athlete is clean"?

After that it is a matter of just writing down Beyes' Theorem and putting the numbers in.

You can probably do all these without directly using Beyes' theorem by assuming you are testing some arbitrary number of athletes.
Say you have 1000 athletes to test:
1. how many do drugs?
2. how many of them will test positive?
3. how many don't do drugs?
4. how many of them test positive?
5. how many positive tests overall?
6. probability that a positive test means the athlete does drugs?
... rinse and repeat.
 
Last edited:

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