Engineering Statistics Question

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SUMMARY

The discussion revolves around calculating the probability of having a disease given a negative test result, using Bayes' theorem. The key probabilities are established: P(D) = 0.005 (prevalence of the disease), P(+|D) = 0.98 (true positive rate), and P(+|D^c) = 0.02 (false positive rate). The user seeks to find P(D|-) and is guided to use the relationship P(D|-) = P(+|D^c)P(D), which leads to the calculation of 1.0E-4 for missed cases. The discussion highlights the importance of understanding conditional probabilities and the notation used in probability theory.

PREREQUISITES
  • Understanding of Bayes' theorem and conditional probability
  • Familiarity with probability notation (e.g., P(A|B))
  • Knowledge of true positive and false positive rates
  • Basic statistics concepts related to disease prevalence
NEXT STEPS
  • Study Bayes' theorem in detail, focusing on its applications in medical testing
  • Learn about the implications of false positives and false negatives in diagnostic tests
  • Explore examples of conditional probability problems to reinforce understanding
  • Investigate the concept of prevalence and its impact on test accuracy
USEFUL FOR

Students in statistics or epidemiology, healthcare professionals involved in diagnostic testing, and anyone interested in understanding the implications of test results in medical contexts.

wolfmanzak
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Homework Statement


The proportion of people in a given community who have a certain disease is 0.005. A test is available to diagnose the disease. If a person has the disease, the probability that the test will produce a positive signal is 0.98. If a person does not have the disease, the probability that the test will produce a positive signal is 0.02.

If a person tests negative, what is the probability that the person actually has the disease?

Homework Equations


I'm at a loss for the relevant equation. I've scanned through my book several times.


The Attempt at a Solution


I set the problem up as follows:
Given the following:
Probability that one has the disease: P(D)=.005
Probability that given one has the disease, they test positive: P(+|D)=0.98
Probability that given one does not have the disease, they test positive: P(+|D^c)=.02

I've used the notation P(-) for test is negative which is equal to P(+^c)

I'm trying to find P(D|-) based on the problem statement. I can't find any way to relate this to what I've been given. The solution says to use P(+|D^c)P(D)=.02*.005=1.0E-4 but I have no idea where that relation came from or where to find a proof of such online.

If anyone could explain to me why P(D|-)=P(+|D^c)P(D) it would really help my understanding of the problem.
 
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Well I agree with the answer at least. Intuitive word explanation: Trying to find how many people who actually have the disease are missed. We know .005 are diseased. If you could test that group, .98*.005=.0049 would be confirmed. (1-.98)*.005=.0001=1e-4 are missed. They have it but got a false test.
I'm still working on the proof. The solutions are wrong sometimes, but in this case I would go with the solutions over me. Hopefully I'll get the same work they did.
 
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First off, for the notation you're using, the | operator is not commutative if I understand it correctly. So P(-|D)=0.02 does not equal P(D|-).
All I can figure out is that maybe there is a law that for good tests P(-|D)=P(+|D^c). To me that doesn't seem reasonable. [edit: actually now it seems somewhat reasonable but still can't help too much]
Sorry I couldn't help you more. Maybe someone else can.
 
Last edited:

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