Consider the experiment of throwing a die, if a multiple of 3 comes up throw the die again and if any other number comes toss a coin. Find the conditional probability of the event “the coin shows a tail”, given that “at least one die shows a 3''.
S={(3,1), (3,2), (3,3), (3,4), (3,5), (3,6), (6,1), (6,2), (6,3), (6,4), (6,5), (6,6), (1,H), (2,H), (3,H), (4,H), (5,H), (1,T), (2,T), (3,T), (4,T), (5,T)}
∴n(S)=20
P (first die shows a multiple of 3) = \(\frac {12}{36}\) = \(\frac 13\)
P (first die shows a number which is not a multiple of 3) = \(\frac 46×\frac 12+\frac 46×\frac 12\) = \(\frac {8}{12}\) = \(\frac 23\)
Let,
A = the coin shows a tail = {(1, T), (2, T), (4, T), (5, T)}
B = at least one die shows a 3 = {(3, 1), (3, 2), (3, 3), (3, 4), (3, 5), (3, 6), (6,3)}
\(A∩B = ϕ\)
\(n(A) = 4\)
\(n(B) = 7\)
\(n(A∩B) = 0\)
\(P(B) =\) \(\frac {6}{36}\) =\(\frac 16\)
\(and,\ P(A∩B)=0\)
\(P(A|B)=\frac {P(A∩B)}{P(B)}\)
\(P(A|B)=\frac {0}{\frac {7}{36}}\)
\(P(A|B)=0\)
Three similar urns \(A,B,C\) contain \(2\) red and \(3\) white balls; \(3\) red and \(2\) white balls; \(1\) red and \(4\) white balls, respectively. If a ball is selected at random from one of the urns is found to be red, then the probability that it is drawn from urn \(C\) is ?
. If a random variable X has the following probability distribution, then the mean of X is:
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If 3 dice are thrown, the probability of getting 10 as the sum of the three numbers on the top faces is ?
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.
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].
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.