Coloured balls are distributed in four boxes as shown in the following table:
| Box | Black | White | Red | Blue |
|---|---|---|---|---|
| I | 3 | 4 | 5 | 6 |
| II | 2 | 2 | 2 | 2 |
| III | 1 | 2 | 3 | 1 |
| IV | 4 | 3 | 1 | 5 |
A box is selected at random and then a ball is randomly drawn from the selected box. The colour of the ball is black, what is the probability that ball drawn is from the box III?
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 (Bayes' Theorem)
We need P(Box III∣Black).
Step 1 – Prior probabilities
Each box is equally likely: P(I)=P(II)=P(III)=P(IV)=41.
Step 2 – Likelihoods (probability of drawing a black ball from each box)
- Box I: 3 black out of 3+4+5+6=18 balls → P(Black∣I)=183=61
- Box II: 2 black out of 8 balls → P(Black∣II)=82=41
- Box III: 1 black out of 1+2+3+1=7 balls → P(Black∣III)=71
- Box IV: 4 black out of 4+3+1+5=13 balls → P(Black∣IV)=134
Step 3 – Total probability of black
P(Black)=41(61+41+71+134)
Compute common denominator (LCM of 6,4,7,13 = 1092):
61=1092182,41=1092273,71=1092156,134=1092336
Sum = 1092182+273+156+336=1092947
Thus P(Black)=41⋅1092947=4368947
Step 4 – Bayes’ Theorem
P(III∣Black)=P(Black)P(III)⋅P(Black∣III)=436894741⋅71=281⋅9474368=947156
The probability that the black ball came from Box III is 947156.
By Bayes' theorem, given the drawn ball is black, P(Box III)=947156.
Let B1,B2,B3,B4 be the events of selecting boxes I–IV, and K the event of drawing a black ball. A box is chosen at random, so P(Bi)=41.
Black-ball probability in each box:
- Box I: 3+4+5+6=18 balls, 3 black ⇒P(K∣B1)=183=61
- Box II: 2+2+2+2=8 balls, 2 black ⇒P(K∣B2)=82=41
- Box III: 1+2+3+1=7 balls, 1 black ⇒P(K∣B3)=71
- Box IV: 4+3+1+5=13 balls, 4 black ⇒P(K∣B4)=134
Total probability of a black ball:
P(K)=41(61+41+71+134)=41⋅1092947=4368947.
Bayes' theorem:
P(B3∣K)=P(K)P(K∣B3)P(B3)=436894771⋅41=7⋅9471092=947156.
The probability that the black ball was drawn from Box III is 947156.
Method: Bayes' Theorem (finding the cause from the observed outcome)
Use this when an item is drawn from one of several containers and, given its property, you want the probability of a particular container. You know the chance of the colour given each box, but want the box given the colour — reversed conditioning.
Steps
Step 1: Set up the partition of possible causes.
List the mutually exclusive, exhaustive hypotheses (here: the ball came from box I, II, III or IV) and write their prior probabilities P(Ei) (these must sum to 1).
Step 2: Write each likelihood.
For each cause, state P(A∣Ei) — the probability of the observed outcome (here: the drawn ball is black) under that cause.
Step 3: Get the total probability of the outcome (the denominator).
By the law of total probability,
P(A)=∑iP(Ei)P(A∣Ei).
Step 4: Apply Bayes' theorem for the cause you want.
P(Ek∣A)=∑iP(Ei)P(A∣Ei)P(Ek)P(A∣Ek).
The numerator is just the one term of the denominator that belongs to the cause you are asked about — so the answer is that cause's share of the total chance of the outcome.
Key subtlety: compute each likelihood against that box's own total number of balls (the boxes here hold different totals), and weight by the equal prior P(box)=41 for random box selection — do not simply pool all black balls across boxes.
Common Mistakes
Mistake 1: Pooling all black balls over all balls.
Why it's wrong: computing total balls3+2+1+4 ignores that a box is chosen first (each with probability 41) and that the boxes hold different totals. Correct approach: use Bayes' theorem over the four equally likely boxes.
Mistake 2: Not dividing each black count by that box's own total.
Why it's wrong: box III has 7 balls, box I has 18 — the black probability differs even for similar counts. Correct approach: P(black∣box)=total in boxblack in box.
Mistake 3: Forgetting the equal prior 41 per box.
Why it's wrong: the box is selected at random, so each prior is 41 and must appear in every term. Correct approach: weight each likelihood by 41 in both numerator and denominator.
Showing the 12 most recent of 24 on this concept.
- 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 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 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 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 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 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 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 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 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 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 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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