Q.If P(A)=52, P(B)=103 and P(A∩B)=51, then P(A′∣B′)⋅P(B′∣A′) is equal to
(A) 65
(B) 75
(C) 4225
(D) 1
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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
We need P(A′∣B′)⋅P(B′∣A′).
By definition, P(A′∣B′)=P(B′)P(A′∩B′) and P(B′∣A′)=P(A′)P(A′∩B′).
So the product is P(A′)⋅P(B′)[P(A′∩B′)]2.
Step 1: Find P(A′) and P(B′)
P(A′)=1−52=53,
P(B′)=1−103=107.
Step 2: Find P(A′∩B′) using De Morgan’s law
P(A′∩B′)=P((A∪B)′)=1−P(A∪B).
P(A∪B)=P(A)+P(B)−P(A∩B)=52+103−51=104+103−102=105=21. …
The problem asks for the product of two conditional probabilities involving complements. Using the definition of conditional probability and De Morgan’s law, we find the value is 4225, which corresponds to option (C).
We are given P(A)=52, P(B)=103, and P(A∩B)=51. We need P(A′∣B′)⋅P(B′∣A′).
The key idea: conditional probability measures the chance of one event given that another has occurred. Here, both conditions involve complements — so we first find probabilities of the complements and their intersection.
Step 1: Find P(A′) and P(B′).
Since P(A′)=1−P(A) and P(B′)=1−P(B):
P(A′)=1−52=53,P(B′)=1−103=107.
Step 2: Find P(A′∩B′).
By De Morgan’s law, A′∩B′=(A∪B)′. So P(A′∩B′)=1−P(A∪B).
We need P(A∪B) first. Using the addition rule:
P(A∪B)=P(A)+P(B)−P(A∩B)=52+103−51.
Convert to tenths: 52=104, 51=102. So:
P(A∪B)=104+103−102=105=21.
Thus:
P(A′∩B′)=1−21=21.
A quick check: P(A′∩B′) is the probability that neither A nor B occurs. Since P(A∪B)=1/2, the complement is also 1/2 — a neat symmetry here.
Step 3: Write the conditional probabilities. …
Method: Conditional probabilities of complements
Use this when the events being conditioned on are complements (A′, B′) and you must combine two such conditionals.
Steps
Step 1: Get the complement marginals.
P(A′)=1−P(A),P(B′)=1−P(B).
Step 2: Get the joint of the complements with De Morgan's law.
A′∩B′=(A∪B)′, so
P(A′∩B′)=1−P(A∪B),withP(A∪B)=P(A)+P(B)−P(A∩B). …
Common Mistakes
Mistake 1: Using P(A′∣B′)=1−P(A∣B).
Why it's wrong: conditional probability does not distribute over complements, and here the condition also changes from B to B′. Correct approach: go back to the definition, P(A′∣B′)=P(B′)P(A′∩B′).
Mistake 2: Mishandling the joint of complements. …
Showing the 12 most recent of 24 on this concept.
- 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 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 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 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 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 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 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 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 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). …
- 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. …
- 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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