What is the probability that a tested detail was faulty?

In summary, the conversation discusses conditional probability in a scenario involving randomly testing details at a factory for faults. The probabilities of faulty details testing faulty and non-faulty details testing faulty are given, and the question asks for the probability of a detail being faulty given that it tested as not faulty. A solution is provided using a sample of 10000 details and the concepts of faulty and not-faulty details. The use of the word "detail" as a translation may not be accurate.
  • #1
Romanka
4
0
Please, help me to solve the problem
Details at a factory are tested randomly to check if they are faulty. It is known from previous experience that the probability of a
faulty detail is known to be 0.03. If a faulty detail is tested the probability of it testing faulty is 0.82. If a non-faulty detail is
tested the probability of it testing faulty is 0.06. Given that the detail was tested as not been faulty, calculate the
probability that it was faulty.

I understand that it's about conditional probability, but can't get it.
Thanks!
 
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  • #2
Have you considered drawing a 2x2 grid and filling in the boxes?
 
  • #3
Romanka said:
Details at a factory are tested randomly to check if they are faulty. It is known from previous experience that the probability of a faulty detail is known to be 0.03. If a faulty detail is tested the probability of it testing faulty is 0.82. If a non-faulty detail is tested the probability of it testing faulty is 0.06. Given that the detail was tested as not been faulty, calculate the probability that it was faulty.
Use $F$ for faulty and $T$ for a positive test.
From the given: $\mathcal{P}(F)=0.03,~\mathcal{P}(T|F)=0.82,~\&~
\mathcal{P}(T|F^c)=0.06$

Now you want $\mathcal{P}{(F|T^c)}$$=\dfrac{\mathcal{P}(F\cap T^c)}{\mathcal{P}(T^c)}$.
 
  • #4
thanks, I'll try



---------- Post added at 03:27 PM ---------- Previous post was at 03:26 PM ----------

Plato said:
Use $F$ for faulty and $T$ for a positive test.
From the given: $\mathcal{P}(F)=0.03,~\mathcal{P}(T|F)=0.82,~\&~
\mathcal{P}(T|F^c)=0.06$

Now you want $\mathcal{P}{(F|T^c)}$$=\dfrac{\mathcal{P}(F\cap T^c)}{\mathcal{P}(T^c)}$.

that's exactly what I need! thank you so much!
 
  • #5
Here's how I like to do problems like this: Imagine there are 10000 details (chosen to avoid fractions).

"It is known from previous experience that the probability of a faulty detail is known to be 0.03."
Okay, so our 10000 sample includes (0.03)(10000)= 300 faulty details and 10000- 300= 9700 that are not faulty.

"If a faulty detail is tested the probability of it testing faulty is 0.82."
Of the 300 faulty details, (0.82)(300)= 246 will test faulty, the other 54 will test not-faulty.

"If a non-faulty detail is tested the probability of it testing faulty is 0.06."
Of the 9700 non-faulty details, (.06)(9700)= 582 will test faulty. 9700- 582= 9118 will test not-faulty.

"Given that the detail was tested as not been faulty, calculate the probability that it was faulty."
There are a total of 9118+ 54= 9172 details that test non-faulty of which 54 are faulty.
 
  • #6
thanks!
 
  • #7
By the way, "detail" doesn't seem like quite the word you want. "Detail" means a small part of something larger. I suspect this was translated from another language and you really wanted "item".
 
  • #8
Yes, maybe. But I got the problem about "details" (Thinking)
 
  • #9
Romanka said:
Yes, maybe. But I got the problem about "details" (Thinking)
YES. But what was language of the question?
Did you use a translation program to post it here?
 

FAQ: What is the probability that a tested detail was faulty?

1. What is conditional probability?

Conditional probability is the likelihood of an event occurring given that another event has already occurred. It takes into account the new information or condition when calculating the probability.

2. How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the joint occurrence of two or more events by the probability of the condition. This can be represented by the formula P(A|B) = P(A and B)/P(B), where A and B are events.

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

Unconditional probability is the likelihood of an event occurring without any prior knowledge or conditions. Conditional probability, on the other hand, takes into account new information or conditions when calculating the probability. In other words, conditional probability is a more specific and adjusted probability based on additional information.

4. How can conditional probability be applied in real life?

Conditional probability can be applied in a variety of real-life situations, such as predicting the weather based on the time of year, or determining the likelihood of a person having a certain disease based on their age and family history. It is also commonly used in fields such as finance, medicine, and sports.

5. What are some common misconceptions about conditional probability?

One common misconception about conditional probability is the belief that it always follows the same pattern, where the probability of A given B is the same as the probability of B given A. However, this is not always the case and depends on the specific events and conditions. Another misconception is that conditional probability can be used to determine causation, when in reality it only shows the relationship between events.

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