Q.An urn contains 5 red and 5 black balls. A ball is drawn at random, its colour is noted and is returned to the urn. Moreover, 2 additional balls of the colour drawn are put in the urn and then a ball is drawn at random. What is the probability that the second ball is red?
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.
Split on the colour of the first draw and use the law of total probability. Initially 5 red and 5 black, so P(R1)=P(B1)=21.
The first ball is returned, then 2 balls of its colour are added, so the urn always holds 12 balls before the second draw:
- After a red first draw: 7 red, 5 black ⇒P(R2∣R1)=127.
- After a black first draw: 5 red, 7 black ⇒P(R2∣B1)=125.
P(R2)=21⋅127+21⋅125=247+245=2412=21.
P(second ball is red)=21.
Condition on the first draw's colour: the returned ball plus 2 same-colour balls make 12 in the urn, giving P(R2∣R1)=127 and P(R2∣B1)=125; the total probability is 21.
Why we condition
The urn's make-up before the second draw depends on the colour of the first draw, so we handle the two cases separately and combine them with the law of total probability.
First draw
The urn starts with 5 red and 5 black (10 balls), so
P(R1)=105=21,P(B1)=21.
Rebuild the urn (the drawn ball is returned)
The drawn ball is put back, and then 2 extra balls of the same colour are added. Either way the urn now holds 10+2=12 balls.
- First red: back to 5 red and 5 black, then +2 red ⇒7 red, 5 black.
P(R2∣R1)=127.
- First black: back to 5 red and 5 black, then +2 black ⇒5 red, 7 black.
P(R2∣B1)=125.
Law of total probability
P(R2)=P(R1)P(R2∣R1)+P(B1)P(R2∣B1)=21⋅127+21⋅125=247+5=2412=21.
Because the urn starts with equal colours, the two conditional probabilities 127 and 125 are symmetric about 21 and average back to 21.
P(second ball is red)=21.
Method: Law of Total Probability (conditioning on the first stage)
Use this whenever the probability you want depends on the unknown outcome of an earlier random stage — draw-then-draw, choose-then-observe, transfer-then-draw.
Steps
Step 1: Identify the "hidden" first stage and list its exhaustive cases.
Find the earlier event whose outcome changes the situation for the event you care about — here, the colour of the first draw. Write those cases as a partition H1,H2,… that are mutually exclusive and cover every possibility, and note each prior P(Hi).
Step 2: For each case, compute the conditional probability of the target.
Freeze yourself inside one case and ask "given this happened, what is the chance of the target now?" — i.e. P(T∣Hi). Rebuild the sample space for that case (recount the urn after the ball is returned and the extra balls added) before reading off the probability.
Step 3: Combine with the total-probability formula.
P(T)=∑iP(Hi)P(T∣Hi).
Each branch contributes "probability of reaching the case" times "probability of the target within the case." This forward calculation is the engine that also sits inside Bayes' theorem, so mastering it here pays off across the whole chapter.
Common Mistakes
Mistake 1: Treating the first draw as "without replacement".
Why it's wrong: the ball is put back before the two extras are added, so the urn holds 10+2=12 balls before the second draw, not 9 or 11. Correct approach: rebuild the urn as 5+5 returned, then +2 of the drawn colour, total 12.
Mistake 2: Adding the 2 balls to the wrong colour, or to both colours.
Why it's wrong: only balls of the colour just drawn are added, so the two branches are asymmetric (7 red vs 5 red). Correct approach: handle the red-first and black-first cases separately, P(R2∣R1)=127 and P(R2∣B1)=125.
Mistake 3: Reporting a single conditional as the final answer.
Why it's wrong: 127 is only the red-first branch. Correct approach: combine both branches with the law of total probability, P(R2)=21⋅127+21⋅125=21.
Showing the 12 most recent of 24 on this concept.
- GUJCET 2025Set 031 markMCQQ.Let A and B be two events such that P(A)=83, P(B)=85 and P(A∪B)=43. Then P(A′∣B)−P(A∣B)= _____. (A) 51 (B) 53 (C) 52 (D) 54
›Reveal solutionSolution
P(A∩B)=P(A)+P(B)−P(A∪B), then use conditional probabilities on B.
P(A∩B)=83+85−43=1−43=41.
P(A∣B)=5/81/4=52,P(A′∣B)=1−52=53.
P(A′∣B)−P(A∣B)=53−52=51.
✓Final answer(A) 51
ANSWER: (A)
- GUJCET 2021Set 151 markMCQQ.If P(A)=116, P(B)=115 and P(A∪B)=117, then P(BA)= (A) 54 (B) 32 (C) 114 (D) 112
›Reveal solutionSolution
Get P(A and B) from inclusion-exclusion, then divide by P(B).
Concept. P(A∩B)=P(A)+P(B)−P(A∪B) and P(A∣B)=P(B)P(A∩B).
Solution. P(A∩B)=116+115−117=114. Then P(A∣B)=5/114/11=54.
✓Final answer(A) 54
ANSWER: (A)
- GUJCET 2026Set x1 markMCQQ.Let A and B be two events such that P(A)=115, P(B)=112 and P(A∪B)=113, then P(A′∣B′)= ______ (A) 98 (B) 53 (C) 21 (D) 92
›Reveal solutionSolution
[!TLDR] P(A′∣B′)=1−P(B)1−P(A∪B)=98.
Concept
By De Morgan's law A′∩B′=(A∪B)′, so P(A′∩B′)=1−P(A∪B). Then the conditional probability is P(A′∣B′)=P(B′)P(A′∩B′) with P(B′)=1−P(B).
Solution
P(A′∩B′)=1−P(A∪B)=1−113=118.
P(B′)=1−P(B)=1−112=119.
P(A′∣B′)=9/118/11=98.
[!ANSWER] (A) 98
- GUJCET 2025Set 031 markMCQQ.A man is known to speak truth 4 out of 5 times. He throws a die and reports that it is a six. The probability that actually there was a six is (A) 95 (B) 94 (C) 355 (D) 354
›Reveal solutionSolution
P(six∣reports six)=P(6)P(T)+P(not 6)P(lie)P(6)P(T).
Let P(6)=61, P(not 6)=65, truth =54, lie =51.
P=61⋅54+65⋅5161⋅54=4/30+5/304/30=94.
✓Final answer(B) 94
ANSWER: (B)
- GUJCET 2022Set 081 markMCQQ.Probability that A speaks truth is 54. A coin is tossed. A reports that a head appears. The probability that actually there was head is ______. (A) 52 (B) 54 (C) 51 (D) 21
›Reveal solutionSolution
A truthful reporter (p = 4/5) on a fair coin makes the reported head almost as reliable as their honesty.
Concept. Bayes' theorem: P(H∣R)=P(R∣H)P(H)+P(R∣T)P(T)P(R∣H)P(H).
Solution. P(H)=P(T)=21. A reports head truthfully with prob 54 (if head) and lies with prob 51 (if tail).
P(H∣R)=21⋅54+21⋅5121⋅54=52+10152=5/104/10=54.
✓Final answer(B) 54
ANSWER: (B)
- GUJCET 2019Set 171 markMCQQ.If 6P(A)=8P(B)=14P(A∩B)=1, then P(BA′)= (A) 74 (B) 53 (C) 73 (D) 52
›Reveal solutionSolution
With P(A)=61,P(B)=81,P(A∩B)=141: P(A′∣B)=1−P(B)P(A∩B)=73.
Concept: From 6P(A)=8P(B)=14P(A∩B)=1: P(B)=81, P(A∩B)=141.
P(A′∣B)=P(B)P(A′∩B)=1−P(B)P(A∩B)=1−1/81/14=1−148=73
✓Final answer(C) 73
ANSWER: (C)
- GUJCET 2026Set x1 markMCQQ.Three cards are drawn successively, without replacement from a pack of 52 well shuffled cards. The probability that first two cards are kings and the third card drawn is an ace is ______ (A) 1352001 (B) 55252 (C) 55253 (D) 1352003
›Reveal solutionSolution
Multiply the successive conditional probabilities for drawing without replacement.
First card king: 524. Second card king (3 kings left of 51): 513. Third card ace (4 aces still present of 50): 504.
P=524⋅513⋅504=13260048=55252.
✓Final answerP=55252
ANSWER: (B)
- GUJCET 2020Set 071 markMCQQ.If A and B are any two events such that P(A)+P(B)−P(A∩B)=P(A) then ________. (A) P(AB)=1 (B) P(BA)=0 (C) P(AB)=0 (D) P(BA)=1
›Reveal solutionSolution
P(A)+P(B)−P(A∩B)=P(A) forces P(A∩B)=P(B), meaning B is contained in A, so P(A/B)=1.
Concept. Rearrange the given equation: P(B)−P(A∩B)=0⇒P(A∩B)=P(B).
Step. Conditional probability: P(A/B)=P(B)P(A∩B)=P(B)P(B)=1.
Meanwhile P(B/A)=P(A)P(A∩B)=P(A)P(B), which need not be 1. So only option (D) is forced.
✓Final answer(D) P(A/B)=1
ANSWER: (D)
- GUJCET 2021Set 151 markMCQQ.If A and B are two events such that P(A)=0 and P(AB)=1, then (A) B⊂A (B) B=∅ (C) A=∅ (D) A⊂B
›Reveal solutionSolution
[!TLDR] Melatonin is a pineal hormone controlling circadian rhythm, not the menstrual cycle. Estrogen, progesterone and relaxin are all reproductive hormones linked to the ovarian cycle.
Concept
In NCERT/CBSE-aligned human reproduction, the menstrual cycle is regulated by pituitary hormones (FSH, LH) and ovarian hormones. Estrogen (secreted by the growing follicle) causes the proliferative phase; progesterone (from the corpus luteum) maintains the secretory endometrium; and relaxin, also a corpus-luteum/reproductive hormone, prepares reproductive tissues. Melatonin, secreted by the pineal gland, governs the day–night biological clock and is not a menstrual-cycle hormone.
Solution
Evaluate each option:
- Progesterone — corpus luteum hormone, central to the cycle. Associated.
- Estrogen — follicular hormone, central to the cycle. Associated.
- Relaxin — a reproductive hormone of the ovary/corpus luteum. Associated.
- Melatonin — pineal hormone for circadian rhythm; not part of the menstrual cycle.
The hormone NOT associated with the menstrual cycle is melatonin.
[!ANSWER] (A) Melatonin
- GUJCET 2026Set x1 markMCQQ.If A and B are any two events such that P(A)+P(B)−P(A and B)=P(A), then ______ (A) P(B∣A′)=1 (B) P(B∣A)=0 (C) P(A∣B)=1 (D) P(A∣B)=0
›Reveal solutionSolution
Simplify the given relation to find P(A∩B)=P(B), i.e. B⊆A effectively.
Given P(A)+P(B)−P(A and B)=P(A), which reduces to:
P(B)−P(A∩B)=0⇒P(A∩B)=P(B).
Therefore:
P(A∣B)=P(B)P(A∩B)=P(B)P(B)=1.
✓Final answerP(A∣B)=1
ANSWER: (C)
- GUJCET 2024Set 131 markMCQQ.If A and B are two events such that P(B)=0 and P(A∣B)=1, then __________. (A) B=ϕ (B) B⊂A (C) A=ϕ (D) A⊂B
›Reveal solutionSolution
P(A∣B)=1 forces P(A∩B)=P(B), meaning B⊂A.
Concept. By definition P(A∣B)=P(B)P(A∩B).
Steps. Given P(A∣B)=1 and P(B)=0,
P(B)P(A∩B)=1⇒P(A∩B)=P(B).
The intersection carrying the whole probability of B means every outcome of B lies in A, i.e. B⊂A.
✓Final answer(B) B⊂A
ANSWER: (B)
- GSEB Higher Secondary Certificate (HSC) Examination 2026Set ANNUAL1 markMCQQ.If P(A/B)>P(A), then which of the following is true?(a) P(B∣A)<P(B)(b) P(A∩B)<P(A)⋅P(B)(c) P(B∣A)>P(B)(d) P(B∣A)=P(B)
›Reveal solutionSolution
Convert the given inequality into one about the joint probability, then flip it around to get P(B∣A).
P(A∣B)>P(A)⇒P(B)P(A∩B)>P(A)⇒P(A∩B)>P(A)P(B).
Dividing by P(A): P(A)P(A∩B)>P(B), i.e. P(B∣A)>P(B).
✓Final answerThe correct option is (c) P(B∣A)>P(B).
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