Conditional Probability

If you look at the first paragraph, it says that a person who is HIV positive would test positive under the ELISA test 95% of the time. But then in the second paragraph, it says that a person who is HIV negative would test positive under the ELISA test 99% of the time. So it seems contradictory. But I think you're right, it should be 1% instead of 99%. In summary, in 1988, the state of Illinois required couples to obtain a marriage license to undergo HIV testing, which consisted of two separate tests, the ELISA test and the Western Blot test. The ELISA test was significantly less expensive and a person who is HIV positive would test positive under this test
  • #1
Darth Frodo
212
1

Homework Statement



In 1988, the state of Illinois required HIV testing for a couple to obtain a marriage
license. The HIV testing at the time consisted of two separate tests, the ELISA
test and the Western Blot test. The Elisa test was signi cantly less expensive. A
person who is HIV positive would test positive under the ELISA test 95% of the
time. A person who is HIV negative would test positive under the ELISA test 99%
of the time. In 1988, it was estimated that the percentage of people applying
for a marriage license that were actually HIV positive was 1%.

If someone test positive on the ELISHA test, then that person is given the West-
ern Blot test. A person who is HIV positive will test positive on the Western
Blot test 99% of the time, while a person who is HIV negative will test positive
on the Western Blot test 5% of the time. What is the conditional probability that someone is HIV positive given that the person tests positive on both the Western Blot and the ELISHA test?



The Attempt at a Solution



Basically I drew a tree diagram to figure this out, but it seems very low. I would appreciate it if someone could tell me where I'm going wrong.

If anyone has any questions on notation, please ask
 

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  • #2
Darth Frodo said:
A person who is HIV negative would test positive under the ELISA test 99%
I think this should be "negative" (or 1% instead of 99%), otherwise the test would be very strange.
While this should change your result to a larger value, small probabilities to have some rare disease are not uncommon in medical tests in general.
 
  • #3
Yes, I think you're right. I completely misread the question. Although, I think my confusion is rather valid.
 

Related to Conditional Probability

1. 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 represented as P(A|B), where A is the event of interest and B is the event that has already occurred.

2. How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the joint occurrence of the two events (P(A∩B)) by the probability of the event that has already occurred (P(B)). This can also be represented as P(A|B) = P(A∩B) / P(B).

3. What is the difference between conditional and unconditional probability?

Unconditional probability, also known as marginal probability, measures the likelihood of an event occurring without any additional information. On the other hand, conditional probability takes into account the occurrence of another event and adjusts the probability accordingly.

4. How is conditional probability used in real life?

Conditional probability is used in various fields such as statistics, finance, and science to make predictions and decisions based on existing data. For example, in medicine, conditional probability can help determine the likelihood of a patient having a certain disease based on their symptoms and medical history.

5. What are some common misconceptions about conditional probability?

One common misconception about conditional probability is that it can only be used for two events. In reality, it can be applied to multiple events. Another misconception is that conditional probability always implies causation, when in fact, it only measures the relationship between events.

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