Q.A couple has two children,
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).
- If P(A∣B)=P(A), then knowing B tells you nothing about A — the events are independent. That is a special case, not the general rule.
Conditional probability is the foundation of the multiplication theorem, independence, and Bayes' theorem — every "given that" question in this chapter rests on it.
Conditional Probability opens the CBSE Class 12 Probability chapter and is foundational for everything that follows in that unit, including Bayes' theorem and the multiplication rule — making "conditional probability formula class 12 with examples" one of the most searched topics in Class 12 Mathematics. It is equally important for JEE Main and CUET, where conditional probability questions are set almost every year.
Concept: Conditional Probability — we restrict the sample space based on the given condition.
(i) Sample space for two children: {MM,MF,FM,FF}.
Given at least one male, the reduced space is {MM,MF,FM}.
Only MM satisfies "both males".
So P=31.
(ii) Given the elder child is female, the reduced space is {FM,FF}.
Only FF satisfies "both females".
So P=21.
- The probability is 31;
- The probability is 21.
Conditional probability reduces the sample space to only outcomes satisfying the given condition. For (i), the probability that both are males given at least one male is 31. For (ii), the probability that both are females given the elder is female is 21.
Concept and Intuition
When we say "given that" something is true, we are no longer looking at all possible outcomes — we restrict our attention to only those outcomes where the condition holds. This is the heart of conditional probability: P(A∣B)=P(B)P(A∩B), where B is the condition.
For a family with two children, the natural sample space (assuming equal probability for male and female, and independence) is:
{MM,MF,FM,FF}
Each outcome has probability 41. The order matters here — first child then second — so MF and FM are distinct.
The classic mistake is to treat "at least one male" as if it only eliminates FF, but then to forget that the remaining three outcomes are not equally likely under the condition? Actually, they are equally likely because each original outcome had equal probability, and we are simply discarding one. So the conditional probability is just counting: number of favorable outcomes in the reduced space divided by total outcomes in the reduced space.
Let's work each part carefully.
(i) Both males, given at least one male
-
Define events.
Let A = "both children are males" = {MM}.
Let B = "at least one child is male" = {MM,MF,FM}.
-
Find the reduced sample space.
The condition B removes only {FF}. So the new sample space has 3 equally likely outcomes: MM, MF, FM.
-
Count favorable outcomes.
Only MM satisfies A. So exactly 1 outcome.
-
Compute probability.
P(A∣B)=∣B∣∣A∩B∣=31.
A common error is to think that "at least one male" means the first child is male, or to list the reduced space as {MM, MF} — forgetting that FM (first female, second male) is also valid. Always list all ordered pairs.
(ii) Both females, given elder child is female
-
Define events.
Let C = "both children are females" = {FF}.
Let D = "the elder child is female" = {FF,FM}.
-
Find the reduced sample space.
Condition D keeps only outcomes where the first child (elder) is female: FF and FM. These are equally likely.
-
Count favorable outcomes.
Only FF satisfies C. So 1 outcome.
-
Compute probability.
P(C∣D)=∣D∣∣C∩D∣=21.
Notice the difference: "at least one male" is a symmetric condition that keeps three outcomes, while "elder is female" is an asymmetric condition that keeps only two. That's why the answers differ — the condition itself determines how much the sample space shrinks.
- The probability that both are males given at least one male is 31.
- The probability that both are females given the elder is female is 21.
Method: Conditional probability by reducing the sample space
For "given that … , find the probability that …" problems with a small set of equally likely outcomes, you can skip the fraction formula and just shrink the world of possibilities.
Steps
Step 1: Write the full, ordered, equally likely sample space.
List every outcome once, keeping order where it matters (elder child first, then younger): {MM,MF,FM,FF}, each equally likely.
Step 2: Keep only the outcomes that satisfy the given condition.
The condition becomes your new, smaller sample space. "At least one male" keeps {MM,MF,FM}; "elder is female" keeps {FF,FM}. Discard everything the condition rules out.
Step 3: Count the favourable outcomes inside the reduced space.
P(target∣condition)=total outcomes in the reduced spacefavourable outcomes in the reduced space.
The whole answer turns on listing the reduced space completely — order-sensitive outcomes like FM versus MF are the ones most often missed.
Common Mistakes
Mistake 1: Reducing "at least one male" to {MM,MF} and getting 21.
Why it's wrong: it drops FM (elder female, younger male), which also has at least one male. Correct approach: the reduced space is {MM,MF,FM}, so the answer is 31.
Mistake 2: Treating "at least one male" as "the elder child is male".
Why it's wrong: those are different conditions that shrink the sample space by different amounts. Correct approach: keep every outcome with a male anywhere, not just elder-male ones.
Mistake 3: In part (ii), forgetting that "elder is female" fixes only the first child.
Why it's wrong: the reduced space is {FF,FM}, not a single outcome. Correct approach: count FF among these two, giving 21.
Showing the 12 most recent of 45 on this concept.
- KCET 2021Set A-11 markMCQQ.Two dice are thrown. If it is known that the sum of numbers on the dice was less than 6 the probability of getting a sum as 3 is (A) 181 (B) 185 (C) 51 (D) 52
›Reveal solutionSolution
We use conditional probability: reduce the sample space to only those outcomes where the sum is less than 6, then find the fraction of those that give a sum of exactly 3. The answer is 51.
The key idea here is conditional probability — the probability of one event given that another event has already occurred. When we say "if it is known that the sum was less than 6", we are no longer considering all 36 possible outcomes of throwing two dice. Instead, we restrict ourselves to only those outcomes that satisfy the condition, and then ask: within this smaller set, what fraction gives a sum of 3?
Let’s work it out step by step.
-
List all possible outcomes when two dice are thrown.
Each die shows a number from 1 to 6. The total number of ordered pairs (a,b) is 6×6=36.
-
Identify the condition: sum less than 6.
The possible sums are 2, 3, 4, and 5 (since sum = 1 is impossible with two dice). Let’s list all ordered pairs that give each sum:
- Sum = 2: (1,1) → 1 outcome
- Sum = 3: (1,2),(2,1) → 2 outcomes
- Sum = 4: (1,3),(2,2),(3,1) → 3 outcomes
- Sum = 5: (1,4),(2,3),(3,2),(4,1) → 4 outcomes
Total outcomes with sum < 6: 1+2+3+4=10.
Watch outA common mistake is to forget that (1,2) and (2,1) are different outcomes. Dice are distinct, so order matters. Always count ordered pairs unless the problem explicitly says "identical dice" (which is rare in such problems).
-
Identify the event of interest: sum = 3.
From the list above, there are exactly 2 outcomes: (1,2) and (2,1).
-
Apply conditional probability.
The probability that the sum is 3, given that the sum is less than 6, is:
P(sum=3∣sum<6)=Number of outcomes with sum<6Number of outcomes with sum=3 and sum<6
Since every outcome with sum = 3 automatically satisfies sum < 6, the numerator is just 2. The denominator is 10.
So:
P=102=51
TipYou can also think of this as: "Out of the 10 equally likely ways to get a sum less than 6, exactly 2 give a sum of 3." That’s the same as 102.
✓Final answerThe correct option is (C) 51.
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- COMEDK 2023Set 2023-E1 markMCQQ.A die is thrown twice and the sum of numbers appearing is observed to be 8 . What is the conditional probability that the number 5 has appeared atleast once? (A) 365 (B) 52 (C) 181 (D) 31
›Reveal solutionSolution
Given the sum is 8, the sample space is the 5 ordered pairs summing to 8; two of them include a 5, giving probability 52.
The ordered outcomes with sum 8 are
(2,6),(3,5),(4,4),(5,3),(6,2)(5 outcomes).
Those in which 5 appears at least once: (3,5) and (5,3) — 2 outcomes.
P(5 appears∣sum=8)=52.
✓Final answerThe correct option is (B) — 52
- 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.
Common denominator 12: 123+122−121=124=31.
So P(P∩Q)=1−31=32.
- Compute the desired conditional probability.
P(P∣Q)=P(Q)P(P∩Q)=6532=32⋅56=1512=54.
Watch outA common mistake is to assume P(P∣Q) and P(Q∣P) are reciprocals or that P(P∩Q) can be found by multiplying P(P) and P(Q) directly — that only works for independent events, which is not the case here.
TipYou can also solve this by constructing a 2×2 probability table. From P(P)=41 and P(P∩Q)=121, fill in the rest systematically. The final answer is the same.
✓Final answerThe correct option is (A).
ANSWER: A
- 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.
First find P(A∣B)=P(B)P(A∩B)=1/21/6=31. Since A and A′ partition the space, P(A∣B)+P(A′∣B)=1, so
P(A′∣B)=1−31=32
Both routes agree. (Note P(A)=31 was not even needed — a useful reminder that a conditional probability given B depends only on how B is split.)
✓Final answerThe correct option is (A) — 32.
ANSWER: A
- COMEDK 2024Set 2024-M1 markMCQQ.A and B each have a calculator which can generate a single digit random number from the set {1,2,3,4,5,6,7,8}. They can generate a random number on their calculator. Given that the sum of the two numbers is 12 , then the probability that the two numbers are equal is (A) 645 (B) 51 (C) 161 (D) 81
›Reveal solutionSolution
We are asked for the conditional probability that two numbers are equal given their sum is 12.
The only equal pair summing to 12 is (6,6), and there are 5 total pairs summing to 12.
So the probability is 51, which corresponds to option (B).
Concept and intuition
This is a classic conditional probability problem: we are not interested in all possible outcomes, only those where the sum is exactly 12. The phrase “given that the sum is 12” means we restrict our universe to those pairs. Then we count how many of those restricted outcomes have the two numbers equal. The trap is to forget to restrict the denominator — many students mistakenly use the total number of all possible pairs (64) instead of only the favorable-sum pairs.
Step-by-step solution
-
Identify the sample space
Each of A and B picks a digit from {1,2,…,8}.
Total possible ordered pairs (a,b): 8×8=64.
-
List all pairs with sum 12
We need a+b=12, with 1≤a,b≤8.
Possible values for a:
- If a=4, then b=8
- If a=5, then b=7
- If a=6, then b=6
- If a=7, then b=5
- If a=8, then b=4
So the pairs are:
(4,8), (5,7), (6,6), (7,5), (8,4)
That’s 5 ordered pairs.
-
Count the favorable outcomes
“The two numbers are equal” means a=b.
Among the sum-12 pairs, the only equal pair is (6,6).
So there is exactly 1 favorable outcome.
-
Apply conditional probability
P(equal∣sum=12)=total pairs with sum 12number of equal pairs with sum 12=51.
Watch outA common mistake is to compute 641 (since only one equal pair overall sums to 12 out of all 64 pairs). But the condition “given sum = 12” changes the denominator to 5, not 64.
TipAlways re-read: “given that the sum is 12” means we only care about the 5 outcomes listed. Conditional probability shrinks the universe.
✓Final answerThe correct option is (B).
ANSWER: B
-
- 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.
P(E∣R)=P(R∣E)P(E)+P(R∣Ec)P(Ec)P(R∣E)P(E)=32⋅73+31⋅7432⋅73=216+214216=106=53.
✓Final answerThe probability that the number is actually even is 53 — option (D).
- COMEDK 2025Set 2025-E1 markMCQQ.Two numbers are selected at random from integers 1 to 9 . If their sum is even, what is the probability that both the numbers are odd? (A) 94 (B) 85 (C) 61 (D) 32
›Reveal solutionSolution
This is a conditional probability problem: given that the sum of two numbers from 1–9 is even, we want the probability both are odd. The answer is 5/8, option (B).
We are selecting two numbers from 1 to 9 without replacement (since "selected at random" from distinct integers usually implies no repetition). The sum is even only if both numbers are odd or both are even. So the condition restricts us to those pairs. The question asks: among those pairs with an even sum, what fraction consists of two odd numbers?
1. Count total possible pairs (without replacement)
From 1 to 9, there are 9 numbers. The number of ways to choose any two distinct numbers is
(29)=36.
2. Count pairs with an even sum
A sum is even when both numbers have the same parity.
-
Odd numbers from 1 to 9: 1, 3, 5, 7, 9 → 5 odds.
Number of odd–odd pairs: (25)=10.
-
Even numbers from 1 to 9: 2, 4, 6, 8 → 4 evens.
Number of even–even pairs: (24)=6.
So total pairs with an even sum:
10+6=16.
3. Apply conditional probability
We want
P(both odd∣sum even)=Number of even-sum pairsNumber of odd–odd pairs=1610=85.
TipA common mistake is to treat this as an unconditional probability and compute 3610, which gives 185 — not even among the options. The condition "given sum is even" changes the denominator from 36 to 16.
Watch outAnother pitfall: forgetting that selection is without replacement. If replacement were allowed, the counts would differ, but here the problem implies distinct integers.
✓Final answerThe correct option is (B).
ANSWER: B
-
- 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.
LCM of 15 and 35 is 105:
152=10514,356=10518,
P(F)=10514+10518=10532.
Step 5 — Apply Bayes.
P(B∣F)=32/10518/105=3218=169.
(Check: P(A∣F)=3214=167, and 169+167=1 ✓. Note option (A) 167 is the trap — it is the probability for temple A.)
✓Final answerThe correct option is (D) — 169.
ANSWER: D
- 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:
P(A∪B)=126+124−121=129=43.
Step 3 — De Morgan's law.
The event "neither A nor B occurs" is exactly the complement of "A or B occurs":
A′∩B′=(A∪B)′.
Hence
P(A′∩B′)=1−P(A∪B)=1−43=41.
✓Final answerThe correct option is (D) — 1/4.
ANSWER: D
- COMEDK 2021Set 2021-B1 markMCQQ.At a certain university 4% of male students are over 6 feet tall and 1% of female students are over 6 feet tall. The total student population is divided in the ratio 3 : 2, in favor of female students. If a student is selected at random from amongst all those over 6 feet tall, what is the probability that the student is a female? (A) 1/3 (B) 2/5 (C) 3/11 (D) 3/5
›Reveal solutionSolution
Bayes' theorem gives P(female∣>6ft)=0.0220.006=113.
Population split 3:2 in favour of females ⇒ P(F)=53=0.6, P(M)=52=0.4.
Tall fractions: P(T∣F)=0.01, P(T∣M)=0.04.
By Bayes' theorem:
P(F∣T)=P(T∣F)P(F)+P(T∣M)P(M)P(T∣F)P(F)=0.01×0.6+0.04×0.40.01×0.6.
=0.006+0.0160.006=0.0220.006=226=113.
✓Final answerThe correct option is (C) — 3/11
- KCET 2020Set A-11 markMCQQ.The probability of solving a problem by three persons A, B and C independently is 21, 41 and 31 respectively. Then the probability of the problem is solved by any two of them is (A) 121 (B) 41 (C) 241 (D) 81
›Reveal solutionSolution
The problem asks for the probability that exactly two of the three persons solve it. We compute the sum of probabilities for each pair solving while the third fails, giving 81.
The key idea: "solved by any two of them" means exactly two solve it, not at least two. The third person must fail. Since A, B, and C work independently, we multiply their individual probabilities for each specific outcome and then add the three possible cases.
A common mistake is to include the case where all three solve it. That would be "at least two", not "any two". The phrase "any two" in probability problems almost always means exactly two.
Let’s denote:
- P(A)=21, so P(A fails)=1−21=21
- P(B)=41, so P(B fails)=1−41=43
- P(C)=31, so P(C fails)=1−31=32
We want exactly two successes. There are three mutually exclusive ways this happens:
-
A and B solve, C fails
Probability = P(A)×P(B)×P(C fails)
=21×41×32=242=121
-
A and C solve, B fails
Probability = P(A)×P(C)×P(B fails)
=21×31×43=243=81
-
B and C solve, A fails
Probability = P(B)×P(C)×P(A fails)
=41×31×21=241
Since these three cases cannot happen together, we add them:
121+81+241
Convert to denominator 24:
242+243+241=246=41
Watch outIf you mistakenly included the case where all three solve it, you would add 21×41×31=241, giving 247 — which is not even among the options. The phrase "any two" excludes the all-three case.
✓Final answerThe probability is 41, which corresponds to option (B).
- COMEDK 2024Set 2024-E1 markMCQQ.A coin is tossed until a head appears or until the coin has been tossed three times. Given that 'head' does not appear on the first toss, what is the probability that the coin is tossed thrice? (A) 21 (B) 83 (C) 81 (D) 41
›Reveal solutionSolution
Given the first toss is a tail, the coin is tossed a third time only if the second toss is also a tail — probability 21.
The coin stops the moment a head appears, or after three tosses. We are told the first toss is a tail (no head on toss 1).
Now the second toss decides:
- Head on toss 2 ⇒ stop after 2 tosses.
- Tail on toss 2 ⇒ a third toss is made.
So a third toss happens exactly when the second toss is a tail:
P(tossed thrice∣first is tail)=P(tail on toss 2)=21
✓Final answerThe probability is 21 — option (A).
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