Q.A fair die is rolled. Consider events E={1,3,5}, F={2,3} and G={2,3,4,5} Find
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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(A∣B)=P(B)P(A∩B), provided P(B)=0.
Step 1: List probabilities.
Since the die is fair, each outcome has probability 61.
P(E)=63=21, P(F)=62=31, P(G)=64=32.
Step 2: Find intersections.
E∩F={3} → P(E∩F)=61.
E∩G={3,5} → P(E∩G)=62=31.
F∩G={2,3} → P(F∩G)=62=31.
E∪F={1,2,3,5} → P(E∪F)=64=32.
(E∪F)∩G={2,3,5} → P((E∪F)∩G)=63=21.
(E∩F)∩G={3} → P((E∩F)∩G)=61.
Step 3: Apply formula.
(i) P(E∣F)=1/31/6=21, P(F∣E)=1/21/6=31. …
Restrict the sample space to the given condition and use P(A∣B)=P(B)P(A∩B). For the fair die: P(E∣F)=21, P(F∣E)=31, P(E∣G)=21, P(G∣E)=32, P((E∪F)∣G)=43, P((E∩F)∣G)=41.
Each outcome of {1,2,3,4,5,6} has probability 61. Given E={1,3,5}, F={2,3}, G={2,3,4,5}:
P(E)=63=21,P(F)=62=31,P(G)=64=32.
(i) P(E∣F) and P(F∣E). E∩F={3}, so P(E∩F)=61.
P(E∣F)=1/31/6=21,P(F∣E)=1/21/6=31.
(ii) P(E∣G) and P(G∣E). E∩G={3,5}, so P(E∩G)=62=31.
P(E∣G)=2/31/3=21,P(G∣E)=1/21/3=32. …
Method: Computing conditional probabilities directly from given event sets
Use this when the events are handed to you as explicit subsets of the sample space and you must evaluate several conditionals, including compound events E∪F and E∩F, and both directions P(A∣B) and P(B∣A).
Steps
Step 1: Fix the outcome probabilities.
For a fair die each of {1,…,6} has probability 61, so any event's probability is (its size)/6. Record P(E),P(F),P(G) once.
Step 2: Build the intersections you will need.
Work out the needed overlaps as sets first — E∩F, E∩G, and for compound conditionals (E∪F)∩G and (E∩F)∩G. Use E∪F = outcomes in either, E∩F = outcomes in both.
Step 3: Apply the definition each time.
P(A∣B)=P(B)P(A∩B). …
Common Mistakes
Mistake 1: Swapping P(E∣F) with P(F∣E).
Why it's wrong: both share the numerator P(E∩F)=61 but divide by different quantities — P(E∣F)=1/31/6=21 while P(F∣E)=1/21/6=31. Correct approach: always divide by the probability of the event written after the bar.
Mistake 2: Forgetting to intersect with G before dividing in part (iii). …
Showing the 12 most recent of 24 on this concept.
- 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). …
- 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. …
- 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. …
- 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. …
- 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 …
- 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). …
- 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). …
- 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. …
- 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. …
- 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: …
- 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). …
- 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).
…
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