Probability Problem Homework: Find P(Defective|Test Positive)

In summary, the conversation discusses the probability of finding a defective component using a testing device and the probability of a randomly selected component being defective within a quality control batch. The first part involves finding the probability of a component testing positive, which is calculated to be 17.2%. The second part involves using Bayes' Theorem to find the probability of a component being defective given that it tests positive, which requires defining events and conditional probabilities.
  • #1
teme92
185
2

Homework Statement



Suppose that upon using a particular testing device, a defective component has a 70% chance of being found as defective, and a non-defective component has just a 10% chance of being found defective. Suppose also that within a quality control batch, the probability of a randomly selected component being defective is 12%.

i)What is the probability that a component selected at random from the batch is found to test positive (i.e. tests as being defective)?

ii)What is the probability that a randomly selected component is defective, given it is found to test positive?

Homework Equations





The Attempt at a Solution



i)I said:

(0.12)(0.7) +(0.88)(.1)=0.172

ii)'m having trouble wrapping my head around this one. Did I use the correct method for the first part? Also any help in the second part would be much appreciated.
 
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  • #2
teme92 said:

Homework Statement



Suppose that upon using a particular testing device, a defective component has a 70% chance of being found as defective, and a non-defective component has just a 10% chance of being found defective. Suppose also that within a quality control batch, the probability of a randomly selected component being defective is 12%.

i)What is the probability that a component selected at random from the batch is found to test positive (i.e. tests as being defective)?

ii)What is the probability that a randomly selected component is defective, given it is found to test positive?

Homework Equations


The Attempt at a Solution



i)I said:

(0.12)(0.7) +(0.88)(.1)=0.172

ii)'m having trouble wrapping my head around this one. Did I use the correct method for the first part? Also any help in the second part would be much appreciated.

This problem is a simple application of Bayes' Theorem. Are you familiar with it?

The answer to the first part is correct, by the way, although it's better to express the probability as a percentage since you're given percentages to begin with.

Before applying Bayes' Theorem, start defining your events and your conditional probabilities, e.g. ##p(D^+)## is probability of a random article being defective and ##p(T^+|D^+)## is probability of test positive IF article is defective. Surely, you've seen this sort of notation?
 

Related to Probability Problem Homework: Find P(Defective|Test Positive)

1. What does "P(Defective|Test Positive)" mean?

"P(Defective|Test Positive)" is known as conditional probability. It represents the probability of an item being defective, given that it has tested positive for a certain test.

2. How do you calculate "P(Defective|Test Positive)"?

The formula for calculating conditional probability is P(A|B) = P(A and B) / P(B), where P(A and B) is the probability of both events occurring and P(B) is the probability of the second event occurring.

3. What information do I need to calculate "P(Defective|Test Positive)"?

To calculate conditional probability, you will need to know the probability of the first event (P(A)), the probability of the second event (P(B)), and the probability of both events occurring (P(A and B)).

4. Can "P(Defective|Test Positive)" be greater than 1?

No, the probability of an event cannot be greater than 1. If "P(Defective|Test Positive)" is greater than 1, it means that there is an error in the calculation.

5. How can I use "P(Defective|Test Positive)" in real-life situations?

Conditional probability can be used to make predictions and decisions in real-life situations, such as predicting the likelihood of a certain disease based on a positive diagnostic test or assessing the probability of a product being defective based on quality control tests.

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