Question:

In a test, an examinee either guesses or copies or knows the answer to a multiple choice question with four choices. The probability that he makes a guess is $\frac{1}{3}$ . The probability that he copies is $\frac{1}{6}$ and the probability that his answer is correct given that he copied it is $\frac{1}{8}$ . The probability that he knew the answer to the question given that he correctly answered it, is

Updated On: Jul 6, 2022
  • $\frac{24}{29}$
  • $\frac{1}{4}$
  • $\frac{3}{4}$
  • $\frac{1}{2}$
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The Correct Option is A

Solution and Explanation

Let $E_1$ be the event that the answer is guessed, $E_2$ be the event that the answer is copied, $E_3$ be the event that the examinee knows the answer and E be the event that the examinee answers correctly. Given $P(E_1) = \frac{1}{3} . P(E_2) = \frac{1}{6}$ Assume that events $E_1, E_2$ & $E_3$ are exhaustive. $\therefore P\left(E_{1}\right) + P\left(E_{2}\right) + P\left(E_{3}\right) = 1$ $ \therefore P\left(E_{3}\right) = 1 -P\left(E_{1}\right) -P\left(E_{2}\right)=1- \frac{1}{3}- \frac{1}{6}= \frac{1}{2}. $ Now, $ P\left(\frac{E}{E_{1}}\right) \equiv$ Probability of getting correct answer by guessing $ = \frac{1}{4} $ (Since 4 alternatives) $ P\left(\frac{E}{E_{2}}\right) \equiv $ Probability of answering correctly by copying $ = \frac{1}{8}$ and $ P\left(\frac{E}{E_{3}}\right) \equiv$ Probability of answering correctly by knowing = 1 Clearly, $ \left(\frac{E_{3}}{E}\right) $ is the event he knew the answer to the question given that he correctly answered it. Using Baye?? theorem $ P\left(\frac{E_{3}}{E}\right) $ $ = \frac{P\left(E_{3}\right).P\left(\frac{E}{E_{3}}\right)}{P\left(E_{1}\right).P\left(\frac{E}{E_{1}}\right)+P\left(E_{2}\right).P\left(\frac{E}{E_{2}}\right) + P\left(E_{3}\right).P\left(\frac{E}{E_{3}}\right)} $ $= \frac{\frac{1}{2}\times1}{\frac{1}{3}\times \frac{1}{4}+\frac{1}{6}\times \frac{1}{8}+\frac{1}{2}\times 1}= \frac{24}{29}$
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Concepts Used:

Conditional Probability

Conditional Probability is defined as the occurrence of any event which determines the probability of happening of the other events. Let us imagine a situation, a company allows two days’ holidays in a week apart from Sunday. If Saturday is considered as a holiday, then what would be the probability of Tuesday being considered a holiday as well? To find this out, we use the term Conditional Probability.

Let’s discuss certain theorems of Conditional Probability:

  1. Let us consider a random experiment where the sample space S is considered as space and two events namely A and B happen there. Then, the formula would be:

P(S | B) = P(B | B) = 1.

Proof of the same: P(S | B) = P(S ∩ B) ⁄ P(B) = P(B) ⁄ P(B) = 1.

[S ∩ B indicates the outcomes common in S and B equals the outcomes in B].

  1. Now let us consider any two events namely A and B happening in a sample space ‘s’, then, P(A ∩ B) = P(A).

P(B | A), P(A) >0 or, P(A ∩ B) = P(B).P(A | B), P(B) > 0.

This theorem is named as the Multiplication Theorem of Probability.

Proof of the same: As we all know that P(B | A) = P(B ∩ A) / P(A), P(A) ≠ 0.

We can also say that P(B|A) = P(A ∩ B) ⁄ P(A) (as A ∩ B = B ∩ A).

So, P(A ∩ B) = P(A). P(B | A).

Similarly, P(A ∩ B) = P(B). P(A | B).

The interesting information regarding the Multiplication Theorem is that it can further be extended to more than two events and not just limited to the two events. So, one can also use this theorem to find out the conditional probability in terms of A, B, or C.

Read More: Types of Sets

 

Sometimes students get confused between Conditional Probability and Joint Probability. It is essential to know the differences between the two.