Q.If P(A)=54 and P(A∩B)=107, then P(B∣A) is equal to
(A) 101
(B) 81
(C) 87
(D) 2017
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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). …
Concept: Conditional Probability — P(B∣A) is the probability of B occurring given that A has already occurred.
Step 1: Recall the formula for conditional probability:
P(B∣A)=P(A)P(A∩B)
Step 2: Substitute the given values:
P(B∣A)=54107 …
Conditional probability P(B∣A) is the fraction of A that also contains B. Using P(B∣A)=P(A)P(A∩B), we get 4/57/10=87. The correct option is (C).
The core idea here is conditional probability — the chance that B happens, given that we already know A has happened. When you condition on A, you shrink the "universe" from the whole sample space to just the part where A occurs. So P(B∣A) is not the raw probability of B; it's the proportion of A that also lies inside B.
The formula that captures this is:
P(B∣A)=P(A)P(A∩B)
This works because P(A) is the new "total" probability in the conditioned world, and P(A∩B) is the part of that world where B also occurs.
Now let's apply it step by step.
-
Identify what's given.
We have P(A)=54 and P(A∩B)=107. Notice that P(A∩B) is already the probability that both A and B happen — exactly the numerator we need.
-
Write the conditional probability formula.
P(B∣A)=P(A)P(A∩B)
- Substitute the numbers.
P(B∣A)=4/57/10
- Simplify the fraction. Dividing by 54 is the same as multiplying by its reciprocal 45: 107×45=10×47×5=4035 …
Method: Conditional probability by direct substitution
Use this whenever a problem gives you a joint probability P(A∩B) together with the probability of the conditioning event, and asks for one conditional probability.
Steps
Step 1: Identify which event is the condition (the "given").
In P(target∣condition), the event after the bar is the condition. Its probability becomes the denominator — it is the new "whole world."
Step 2: Write the definition. …
Common Mistakes
Mistake 1: Dividing by the wrong marginal.
Why it's wrong: for P(B∣A) the conditioning event is A, so the denominator must be P(A), not P(B) (which isn't even given here). Correct approach: match the denominator to the event after the bar — P(B∣A)=P(A)P(A∩B). …
Showing the 12 most recent of 45 on this concept.
- KCET 2021Set A-11 markMCQQ.Given that A and B are two events such that P(B)=53, P(A/B)=21 and P(A∪B)=54, then P(A)= (A) 103 (B) 21 (C) 51 (D) 53
›Reveal solutionSolution
Turn the conditional probability into P(A∩B), then substitute into the addition theorem and solve for P(A).
Step 1 — Extract P(A∩B) from the conditional probability.
By definition,
P(A/B)=P(B)P(A∩B)(P(B)=0).
Rearranging (the multiplication theorem):
P(A∩B)=P(A/B)⋅P(B)=21×53=103.
This is the key move — the conditional probability is not directly usable in the union formula, but P(A∩B) is.
Step 2 — Apply the addition theorem.
P(A∪B)=P(A)+P(B)−P(A∩B).
The intersection is subtracted because the elements common to A and B would otherwise be counted twice.
Step 3 — Substitute the known values.
54=P(A)+53−103.
Step 4 — Solve for P(A). …
- COMEDK 2025Set 2025-A1 markMCQQ.If for two events A and B,P(A−B)=51 and P(A)=53 then P(B/A)= (A) 32 (B) 21 (C) 53 (D) 52
›Reveal solutionSolution
The key is to interpret P(A−B) as P(A∩Bc) and use the definition of conditional probability. The result is P(B/A)=32, so option (A) is correct.
We are asked for P(B/A), the probability of B given A. The definition is
P(B/A)=P(A)P(A∩B).
We know P(A)=53, so we need P(A∩B). The given P(A−B)=51 is the key: A−B means “A and not B,” i.e., A∩Bc.
- Relate P(A−B) to P(A∩B) Since A is the union of the disjoint parts “A and B” and “A and not B,” we have
P(A)=P(A∩B)+P(A∩Bc).
Here P(A∩Bc)=P(A−B)=51 and P(A)=53.
- Solve for P(A∩B)
53=P(A∩B)+51⇒P(A∩B)=53−51=52.
- Apply the conditional probability formula P(B/A)=P(A)P(A∩B)=3/52/5=32. …
- COMEDK 2023Set 2023-E1 markMCQQ.
[!FORMULA] If P(B)=53P(A/B)=21 and P(A∪B)=54 then P(A∪B)′+P(A′∪B)=
(A) 54 (B) 21 (C) 1 (D) 51›Reveal solutionSolution
Step 5: sum = 1/5 + 4/5 = 1.
Concept: conditional probability, addition theorem, complements, De Morgan.
Given P(B) = 3/5, P(A|B) = 1/2, P(A U B) = 4/5.
Step 1: P(A ^ B) = P(A|B) P(B) = (1/2)(3/5) = 3/10.
Step 2: from P(A U B) = P(A) + P(B) - P(A ^ B),
P(A) = 4/5 - 3/5 + 3/10 = 1/5 + 3/10 = 1/2.
Step 3: P((A U B)') = 1 - P(A U B) = 1 - 4/5 = 1/5. …
- COMEDK 2025Set 2025-E1 markMCQQ.If A and B are two events such that P(Aˉ)=0.3,P(B)=0.4,P(A∩Bˉ)=0.5, then find the value of P(B/A∪Bˉ) (A) 0.33 (B) 0.7 (C) 0.8 (D) 0.25
›Reveal solutionSolution
The problem asks for a conditional probability P(B∣A∪Bˉ). Using the definition of conditional probability and the given probabilities, we compute P(B∩(A∪Bˉ)) and P(A∪Bˉ), then divide. The final value is 0.25, so the correct option is (D).
We are given:
- P(Aˉ)=0.3, so P(A)=1−0.3=0.7.
- P(B)=0.4.
- P(A∩Bˉ)=0.5.
We need P(B∣A∪Bˉ), which is the probability that B occurs given that A or Bˉ (or both) occurs.
Concept and intuition:
Conditional probability P(X∣Y)=P(Y)P(X∩Y). Here X=B and Y=A∪Bˉ. So we need the probability that both B and (A∪Bˉ) happen, divided by the probability that A∪Bˉ happens. The key is to simplify the intersection B∩(A∪Bˉ) using set algebra — it often collapses to something simpler.
-
Simplify the numerator P(B∩(A∪Bˉ))
Using the distributive law:
B∩(A∪Bˉ)=(B∩A)∪(B∩Bˉ)
But B∩Bˉ=∅, so this is just B∩A.
Hence P(B∩(A∪Bˉ))=P(A∩B).
-
Find P(A∩B)
We know P(A)=0.7 and P(A∩Bˉ)=0.5.
Since A=(A∩B)∪(A∩Bˉ) and these are disjoint,
P(A)=P(A∩B)+P(A∩Bˉ)
⇒0.7=P(A∩B)+0.5
⇒P(A∩B)=0.2.
-
Find the denominator P(A∪Bˉ)
Use the inclusion-exclusion principle: …
- KCET 2025Set A-11 markMCQQ.If A and B are two events such that A⊂B and P(B)=0, then which of the following is correct? (A) P(A∣B)=P(A)P(B) (B) P(A∣B)<P(A) (C) P(A∣B)≥P(A) (D) P(A)=P(B)
›Reveal solutionSolution
Use A⊂B⇒A∩B=A, then note that dividing P(A) by P(B)≤1 cannot make it smaller.
Step 1 — Simplify the intersection.
If every element of A lies in B, then A∩B=A. Hence
P(A∣B)=P(B)P(A∩B)=P(B)P(A).
Step 2 — Compare with P(A).
Every probability satisfies 0<P(B)≤1 (we are told P(B)=0). Therefore P(B)1≥1, and multiplying the non-negative number P(A) by a factor ≥1 gives
P(A∣B)=P(A)⋅P(B)1≥P(A).
Equality holds exactly when P(B)=1 (or when P(A)=0).
Intuition: conditioning on B throws away all outcomes outside B — but none of A lies outside B. So A's share of the shrunken sample space can only grow.
Step 3 — Eliminate. …
- COMEDK 2024Set 2024-A1 markMCQQ.P and Q are considering to apply for a job. The probability that P applies for the job is 41. The probability that P applies for the job given that Q applies for the job is 21, and the probability that Q applies for the job given that P applies for the job is 31. Then the probability that P does not apply for the job given that Q does not apply for the job is (A) 54 (B) 87 (C) 65 (D) 1211
›Reveal solutionSolution
We are given conditional probabilities and need to find P(P∣Q). Using the definitions of conditional probability and the law of total probability, we compute P(Q) and P(Q), then apply Bayes' theorem to get 54, which corresponds to option (A).
Concept and intuition:
This is a classic problem of working backwards from conditional probabilities to find a joint probability table. We know P(P), P(P∣Q), and P(Q∣P). From these, we can find P(P∩Q) in two ways, which lets us solve for P(Q). Then we can compute the desired conditional probability P(P∣Q) using the complement rule and the definition of conditional probability.
Step-by-step solution:
- Write down what is given. Let P = event that P applies, Q = event that Q applies. We have:
P(P)=41,P(P∣Q)=21,P(Q∣P)=31.
- Use the definition of conditional probability to express P(P∩Q) in two ways. From P(P∣Q)=P(Q)P(P∩Q), we get
P(P∩Q)=P(P∣Q)⋅P(Q)=21P(Q).
From P(Q∣P)=P(P)P(P∩Q), we get
P(P∩Q)=P(Q∣P)⋅P(P)=31⋅41=121.
- Equate the two expressions for P(P∩Q) to find P(Q).
21P(Q)=121⇒P(Q)=61.
- Find P(Q) and P(P∩Q).
P(Q)=1−P(Q)=1−61=65.
Also, P(P∩Q)=P(P)−P(P∩Q)=41−121=123−121=122=61.
- Find P(P∩Q). Since P∩Q is the complement of P∪Q, we can use:
P(P∩Q)=1−P(P∪Q).
First, P(P∪Q)=P(P)+P(Q)−P(P∩Q)=41+61−121. …
- KCET 2025Set A-11 markMCQQ.Meera visits only one of the two temples A and B in her locality. Probability that she visits temple A is 52. If she visits temple A, 31 is the probability that she meets her friend, whereas it is 72 if she visits temple B. Meera met her friend at one of the two temples. The probability that she met her at temple B is (A) 167 (B) 165 (C) 163 (D) 169
›Reveal solutionSolution
The friend has already been met (the effect); we want the probability of the cause (temple B) — that reversal of conditioning is exactly Bayes' theorem.
Step 1 — Name the events.
Let A = "Meera visits temple A", B = "Meera visits temple B", F = "she meets her friend".
She visits only one of the two temples, so A and B are mutually exclusive and exhaustive:
P(A)=52⟹P(B)=1−52=53.
Given: P(F∣A)=31, P(F∣B)=72.
Step 2 — Why Bayes.
We are told the outcome (F happened) and asked for the probability of a cause (B). Bayes' theorem inverts the conditioning:
P(B∣F)=P(A)P(F∣A)+P(B)P(F∣B)P(B)P(F∣B).
The denominator is P(F) by the law of total probability — the friend can be met on either branch.
Step 3 — Compute the two branch probabilities.
P(A∩F)=52×31=152,P(B∩F)=53×72=356.
Step 4 — Total probability of meeting the friend. …
- KCET 2019Set A-11 markMCQQ.A man speaks truth 2 out of 3 times. He picks one of the natural numbers in the set S={1,2,3,4,5,6,7} and reports that it is even. The probability that it is actually even is (A) 52 (B) 51 (C) 101 (D) 53
›Reveal solutionSolution
By Bayes' theorem the probability is 53 — option (D).
The set S={1,2,3,4,5,6,7} has 3 even numbers (2,4,6) and 4 odd numbers, and one number is picked at random.
Let E = "the number is even" and R = "the man reports it as even."
Priors.
P(E)=73,P(Ec)=74.
Likelihoods. He tells the truth with probability 32 and lies with probability 31.
- If the number really is even, a truthful report says "even": P(R∣E)=32.
- If the number is odd, only a lie reports "even": P(R∣Ec)=31.
Bayes' theorem. …
- KCET 2022Set C-41 markMCQQ.If A and B are two events such that P(A)=21, P(B)=31 and P(A∣B)=41, then P(A′∩B′) is (A) 3/16 (B) 1/12 (C) 3/4 (D) 1/4
›Reveal solutionSolution
Get P(A∩B) from the conditional probability, use the addition rule for P(A∪B), then apply De Morgan: P(A′∩B′)=1−P(A∪B).
Step 1 — Recover the joint probability from the conditional.
Conditional probability is defined by P(A∣B)=P(B)P(A∩B) — it re-scales the probability of A to the reduced sample space B. Rearranging gives the multiplication rule:
P(A∩B)=P(A∣B)P(B)=41×31=121.
Step 2 — Addition rule for the union.
P(A∪B)=P(A)+P(B)−P(A∩B)=21+31−121.
Taking LCM 12: …
- KCET 2025Set A-11 markMCQQ.If A and B are two non-mutually exclusive events such that P(A∣B)=P(B∣A), then (A) A⊂B but A=B (B) A=B (C) A∩B=ϕ (D) P(A)=P(B)
›Reveal solutionSolution
Write both conditional probabilities with the same numerator P(A∩B), cancel it (legal because the events are not mutually exclusive), and the denominators must be equal.
Step 1 — Definition of conditional probability.
P(A∣B)=P(B)P(A∩B),P(B∣A)=P(A)P(B∩A).
Note A∩B=B∩A, so the two fractions share the same numerator.
Step 2 — Impose the given condition.
P(B)P(A∩B)=P(A)P(A∩B).
Step 3 — Cancel — and see why we are allowed to.
The events are non-mutually-exclusive, i.e. A∩B=ϕ and P(A∩B)=0. (This hypothesis is exactly what makes the cancellation valid — if P(A∩B) were 0, both sides would be 0 for any P(A),P(B) and nothing would follow.) Dividing both sides by P(A∩B):
P(B)1=P(A)1⟹P(A)=P(B).
Step 4 — Why the other options are not forced. …
- KCET 2020Set A-11 markMCQQ.If A and B are two events such that P(A)=31, P(B)=21 and P(A∩B)=61, then P(A′/B) is (A) 32 (B) 31 (C) 21 (D) 121
›Reveal solutionSolution
Use P(A′∣B)=1−P(A∣B) (equivalently P(B)P(B)−P(A∩B)), which gives 32.
Step 1 — The definition of conditional probability.
For P(B)>0,
P(A′∣B)=P(B)P(A′∩B)
Conditioning on B means we restrict the sample space to B and ask what fraction of B lies outside A.
Step 2 — Split B into the part inside A and the part outside A.
The events A∩B and A′∩B are disjoint and their union is exactly B:
P(B)=P(A∩B)+P(A′∩B)
⇒P(A′∩B)=P(B)−P(A∩B)=21−61=63−1=62=31
Step 3 — Divide by P(B).
P(A′∣B)=P(B)P(A′∩B)=2131=31×12=32
Step 4 — Cross-check with the complement rule. …
- COMEDK 2021Set 20211 markMCQQ.A student answers a multiple choice question with 5 alternatives, of which exactly one is correct. The probability that he knows the correct answer is p,0<p<1. If he knows the answer, he answers it correctly; if he does not know the answer, he guesses it, and the probability that he guesses correctly is 51. Given that he has answered the question correctly, the probability that he knew the correct answer is (A) 4p+33p (B) 3p+25p (C) 4p+15p (D) 3p+14p
›Reveal solutionSolution
By Bayes: P(K | correct) = P(K) P(correct | K) / P(correct) = p / [ (4p + 1)/5 ] = 5p/(4p + 1).
Concept: Bayes' theorem. (The stem is truncated, but this is the standard question: given that he answered correctly, find the probability that he actually knew the answer.)
Let K = he knows the answer, P(K) = p, so P(not K) = 1 - p.
If he knows it, he is certainly correct: P(correct | K) = 1.
If he does not know it, he guesses among 5 alternatives: P(correct | not K) = 1/5. …
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