Q.A man is known to speak truth 3 out of 4 times. He throws a die and reports that it is a six. Find the probability that it is actually a six.
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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 (Bayes' Theorem)
Let E1 be the event that the die shows a six, and E2 be the event that it does not show a six.
Let A be the event that the man reports a six.
Step 1: Prior probabilities
P(E1)=61, P(E2)=65.
Step 2: Likelihoods (probability of reporting a six given the actual outcome)
If it is a six, he tells truth: P(A∣E1)=43.
If it is not a six, he lies and reports a six: P(A∣E2)=41.
Step 3: Apply Bayes' Theorem …
A Bayes' theorem problem. The probability the die actually shows a six, given the report, is 83.
Let S be the event "the die shows a six" and R the event "the man reports a six".
P(S)=61,P(S′)=65.
He speaks truth 3 of 4 times, so if it is a six he correctly reports six with probability 43, and if it is not a six he (lying) reports six with probability 41:
P(R∣S)=43,P(R∣S′)=41.
By Bayes' theorem, …
Method: Bayes' Theorem (finding the cause from the observed outcome)
Use this "reliability of a witness" pattern: someone reports an outcome and you want the probability the outcome is genuinely what they claim. You know how truthful the reporter is; you want P(event true∣event reported).
Steps
Step 1: Set up the partition of possible causes.
List the mutually exclusive, exhaustive hypotheses (here: the die actually shows a six, or it does not) 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 man reports a six) 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. …
Common Mistakes
Mistake 1: Answering 43 (his truth-telling rate).
Why it's wrong: 43 is P(reports six∣it is a six), whereas the question asks the reverse, P(it is a six∣reports six). Correct approach: apply Bayes' theorem.
Mistake 2: Forgetting the "lie" branch in the 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. …
- 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 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 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 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 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 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 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). …
- 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). …
- 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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