Conditional Probability: an incorrect error detection

You can calculate P(D-) as the sum of the products of P(D-|Si) and P(Si). You know P(D-|Si) (it is 0.03), and P(Si) is the probability of selecting a chip from the set of all chips that is sound. What is P(Si)?In summary, the problem involves calculating the probability that a chip declared faulty is actually sound, given information about the probabilities of detecting defects and declaring chips sound. Using the formula for conditional probability and the total probability formula, we can calculate this probability by finding the probability of a sound chip being tested and the probability of a chip being declared faulty, then using these values to calculate the desired probability.
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Homework Statement


From Mathematical Statistics and Data Analysis 3ed, Rice

1.8 #61
Suppose chips are tested and the probability they are detected if defective is 0.95, and the probability they are declared sound if they are sound is 0.97. If 0.005 of the chips are faulty. What is the probability that a chip that is declared faulty is sound?


Homework Equations



P(A|B) = P(A[itex]\cap[/itex]B) / P(B)

P(A) = [itex]\Sigma[/itex]P(A|Bi)P(Bi)

The Attempt at a Solution



Let D- be the event a fault is detected
Let D+ be the event no fault is detected
Let Df be the event a chip is faulty
Let S be the event a chip is sound

P(D-|Df) = 0.95
P(D+|S) = 0.97
P(Df) = 0.005
P(S) = 1 - P(Df) = 0.995

Find P(S|D-) (the answer given is 0.86)

P(S|D-) = P(D-[itex]\cap[/itex]S) / P(D-) = P(D-|S)P(S) / P(D-)

So here's where I've been stuck.
First I'd like to know if I've translated the problem correctly.
Secondly how do I find P(D-|S) and P(D-) or am I going about this the wrong way?
 
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  • #2
P(D-|S) is simple. Suppose a sound chip is tested. There are two possible outcomes of this test, that a defect is or is not detected. These events are D+|S and D-|S. Since you know the probability of one of those outcomes, obtaining the other is trivial.

P(D-) is also simple. Use the formula of total probability, the second of your relevant equations.
 
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Related to Conditional Probability: an incorrect error detection

1. What is conditional probability?

Conditional probability is a statistical concept that measures the likelihood of an event occurring, given that another event has already occurred. It is expressed as P(A|B), where A is the event of interest and B is the condition.

2. How is conditional probability used in error detection?

In error detection, conditional probability is used to determine the likelihood of an incorrect detection. This is done by calculating the probability of an error occurring, given that the detection was incorrect. This information can then be used to improve error detection methods.

3. What is an incorrect error detection?

An incorrect error detection refers to a situation where an error is detected, but in reality, there is no error present. This can happen due to various factors such as faulty detection methods, human error, or incomplete data.

4. How can we improve our understanding of conditional probability in error detection?

To improve our understanding of conditional probability in error detection, we can study statistical models and methods, attend workshops and conferences, and collaborate with other scientists in the field. It is also important to continuously evaluate and improve our error detection methods based on new findings and data.

5. What are some common challenges in using conditional probability for error detection?

Some common challenges in using conditional probability for error detection include identifying all relevant variables and their relationships, dealing with incomplete or inaccurate data, and interpreting the results correctly. It is also important to consider the potential biases and limitations of the data and methods used.

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