Q.If probabilities P(A) = \frac{5}{12}, P(B) = \frac{1}{2} and P(A \cap B) = \frac{1}{3}, evaluate P(A/B).
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🔒 Start your 14-day free trial to unlock the full solution →Concept understanding — Conditional Probability
Conditional Probability
Roll a die and ask "what is the chance of an even number?" — that is 3/6. But suppose someone tells you the result is greater than 3. Now you are no longer looking at all six faces, only at {4,5,6}, and two of those (4 and 6) are even, so the probability becomes 2/3. That change — from the probability of A to the probability of A given that B has already occurred — is conditional probability.
The Idea: Shrink the Sample Space
Conditioning on B throws away every outcome where B is false and treats B as the new "whole world." You measure A only against what is still possible.
Think of filtering a table of data: unconditional probability uses every row; conditional probability keeps only the rows where the condition is true.
The Definition
For events A and B with P(B)>0,
P(A∣B)=P(B)P(A∩B).
We divide by P(B) to rescale so that B itself has probability 1; the surviving part of A is the overlap A∩B. Checking the die: P(A∩B)=P({4,6})=62 and P(B)=63, so P(A∣B)=3/62/6=32, matching the intuition.
Rearranging gives the multiplication rule P(A∩B)=P(A∣B)P(B), which is usually the easier way to compute a joint probability when a problem says "given that."
Two Cautions
- P(A∣B) and P(B∣A) are generally not equal; swapping them is the classic mistake. They are linked by Bayes' theorem, P(A∣B)=P(B)P(B∣A)P(A). …
By definition of conditional probability, P(A∣B)=P(B)P(A∩B)=1/21/3=32 …
P(A∣B)=32.
Concept. Conditional probability: P(A∣B)=P(B)P(A∩B), provided P(B)=0.
Steps.
- Given P(A∩B)=31 and P(B)=21. …
Showing the 12 most recent of 88 on this concept.
- CBSE 2026Set 65/1/11 markMCQQ.Assertion (A): In an experiment of throwing an unbiased die, the probability of getting a prime number given that the number appearing on the die is odd is 32. Reason (R): For any two events A and B, P(A∣B)=P(B)P(A∪B). (A) Both Assertion (A) and Reason (R) are true and Reason (R) is the correct explanation of the Assertion (A). (B) Both Assertion (A) and Reason (R) are true but Reason (R) is not the correct explanation of the Assertion (A). (C) Assertion (A) is true and Reason (R) is false. (D) Assertion (A) is false and Reason (R) is true.
›Reveal solutionSolution
The assertion is true: given the outcome is odd, the probability it is a prime is 32. The reason states the correct conditional probability formula. Since the reason directly justifies the calculation in the assertion, both are true and the reason is the correct explanation.
Concept first — Conditional probability asks: If we already know that event B has occurred, what is the probability that event A also occurs? The sample space shrinks from all possible outcomes to just those in B. The formula P(A∣B)=P(B)P(A∩B) is the precise way to compute this reduced probability.
Here, the die is unbiased, so each face {1,2,3,4,5,6} has probability 61. The assertion involves two events:
- A: the number is prime. On a die, the primes are 2,3,5.
- B: the number is odd. The odd numbers are 1,3,5.
The condition "given that the number is odd" means we restrict attention to B={1,3,5}. Among these three equally likely outcomes, the primes are 3 and 5 — that's two out of three. So the conditional probability is 32.
Now let's verify step by step using the formula in Reason (R).
-
Define the events precisely.
A={2,3,5}, B={1,3,5}.
The sample space S={1,2,3,4,5,6}.
-
Compute P(B).
B has 3 outcomes, each with probability 61, so P(B)=63=21.
-
Compute P(A∩B).
A∩B = numbers that are both prime and odd = {3,5}. That's 2 outcomes, so P(A∩B)=62=31.
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Apply the formula from Reason (R).
P(A∣B)=P(B)P(A∩B)=1/21/3=31×12=32.
This matches the assertion exactly. …
- CBSE 2026Set V11 markQ.Choose from [0,3,−1,2,−2,1]. If F is an event of a sample space S then P(S∣F)= ____.
›Reveal solutionSolution
Since S∩F=F, the conditional probability P(S∣F)=1.
By the definition of conditional probability (with P(F)=0),
P(S∣F)=P(F)P(S∩F). …
- CBSE 2026Set CX1 markMCQQ.If 3P(A)=P(B)=135 and P(A/B)=52, then P(A∪B) will be:(a) 3920(b) 3916(c) 3911(d) 3914
›Reveal solutionSolution
Using P(A∩B)=P(A/B)P(B) and the addition rule gives P(A∪B)=3914 — option (d).
Given: 3P(A)=P(B)=135 and P(A/B)=52.
So P(B)=135 and P(A)=31⋅135=395.
Intersection (multiplication rule):
P(A∩B)=P(A/B)P(B)=52⋅135=132.
…
- CBSE 2026Set A1 markMCQQ.P(A)=137, P(B)=139, P(A∩B)=134⇒P(A/B)=(a) 94(b) 74(c) 1312(d) 61
›Reveal solutionSolution
P(A∣B)=94.
Use the conditional-probability definition:
P(A∣B)=P(B)P(A∩B).
Substitute the given values: …
- CBSE 2026Set ANNUAL1 markMCQQ.If P(B)=0.5 and P(A∩B)=0.32, then write the value of P(A∣B).(a) 2315(b) 2516(c) 2716(d) 2316
›Reveal solutionSolution
By the definition of conditional probability, P(A∣B)=P(B)P(A∩B)=2516.
The conditional probability of A given B is defined as
P(A∣B)=P(B)P(A∩B),P(B)eq0
…
- CBSE 2026Set ANNUAL1 markQ.A family has two children. What is the probability that both the children are boys given that at least one of them is a boy?
›Reveal solutionSolution
List the equally likely outcomes for two children, restrict to those with at least one boy, then find the fraction that are both boys.
Sample space ={BB,BG,GB,GG}, each equally likely.
Given at least one boy: reduced sample space ={BB,BG,GB} (3 outcomes).
…
- CBSE 2026Set ANNUAL1 markMCQQ.If P(A)=0.8, P(B)=0.5 and P(AB)=0.4 then P(A∩B)=(a) 0.8(b) 0.5(c) 0.32(d) 0.4
›Reveal solutionSolution
Use the multiplication rule of conditional probability: P(A∩B)=P(B∣A)⋅P(A).
Given P(A)=0.8, P(B∣A)=0.4.
P(A∩B)=P(B∣A)⋅P(A)=0.4×0.8=0.32.
…
- CBSE 2026Set ANNUAL1 markMCQQ.If P(A)=103, P(B)=52 and P(A∪B)=53, then P(B/A) is:(a) 41(b) 31(c) 125(d) 127
›Reveal solutionSolution
Find P(A∩B) from the addition rule, then use the conditional probability formula.
Given P(A)=103, P(B)=52, P(A∪B)=53.
…
- CBSE 2026Set ANNUAL1 markQ.A doctor is to visit a patient. From past experience it is known that the probabilities that he will come by train, bus, scooter or by other means of transport are respectively 103,51,101 and 52. The probabilities that he will be late are 41,31 and 121, if he comes by train, bus and scooter respectively, but if he comes by other means of transport, then he will not be late. Probability that he will not be late, when he comes by other means of transport.
›Reveal solutionSolution
The problem statement itself states that if the doctor comes by other means, he will never be late.
Let E1,E2,E3,E4 denote the events that the doctor comes by train, bus, scooter, or other means, with P(E1)=103, P(E2)=51, P(E3)=101, P(E4)=52 (these sum to 1, confirming they form a complete set of cases).
…
- CBSE 2026Set ANNUAL1 markMCQQ.If P(A) = 1/2, P(B) = 3/8 and P(A∪B) = 27/40 then P(A/B) is equal to:(a) 2/5(b) 8/15(c) 2/3(d) 5/8
›Reveal solutionSolution
First find P(A∩B) using the addition rule, then apply the conditional probability formula P(A/B)=P(B)P(A∩B).
Given P(A)=21, P(B)=83, P(A∪B)=4027.
Using the addition rule P(A∪B)=P(A)+P(B)−P(A∩B):
P(A∩B)=P(A)+P(B)−P(A∪B)=4020+4015−4027=408=51
…
- CBSE 2026Set ANNUAL1 markMCQQ.Assertion (A): If P(A) = 0.8, P(B) = 0.5 and P(B/A) = 0.4 then P(A∩B) = 0.32 Reason (R): Conditional Probability of 'B' when event A has occurred is given by P(B/A) = P(A∩B) / P(A)(a) Both Assertion (A) and Reason (R) are true and Reason (R) is the correct explanation of Assertion (A).(b) Both Assertion (A) and Reason (R) are true, but Reason (R) is not the correct explanation of Assertion (A).(c) Assertion (A) is true but Reason (R) is false.(d) Both Assertion (A) and Reason (R) are false.
›Reveal solutionSolution
Applying the conditional-probability formula in R directly to the given numbers reproduces the value claimed in A.
Reason (R): P(B/A)=P(A)P(A∩B) is the standard definition of conditional probability — true.
Checking Assertion (A): Given P(A)=0.8, P(B/A)=0.4. Rearranging R's formula:
P(A∩B)=P(B/A)×P(A)=0.4×0.8=0.32. …
- CBSE 2026Set ANNUAL1 markMCQQ.If P(A)=21 and P(B)=0, then P(A∣B) is(a) 0(b) 21(c) 1(d) Not defined
›Reveal solutionSolution
Conditional probability is defined as P(A∣B)=P(B)P(A∩B), which requires dividing by P(B) — impossible when P(B)=0.
Given P(A)=21, P(B)=0.
By definition:
P(A∣B)=P(B)P(A∩B)
…
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