Q.Bag I contains 3 red and 4 black balls while another Bag II contains 5 red and 6 black balls. One ball is drawn at random from one of the bags and it is found to be red. Find the probability that it was drawn from Bag II.
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)
Let E1 = choosing Bag I, E2 = choosing Bag II, and A = drawing a red ball.
- P(E1)=P(E2)=21
- P(A∣E1)=73, P(A∣E2)=115
We need P(E2∣A).
Step 1: Apply Bayes' theorem:
P(E2∣A)=P(E1)⋅P(A∣E1)+P(E2)⋅P(A∣E2)P(E2)⋅P(A∣E2)
Step 2: Substitute values:
P(E2∣A)=21⋅73+21⋅11521⋅115=73+115115
Step 3: Simplify denominator:
73+115=7733+35=7768
Thus,
P(E2∣A)=68/775/11=115×6877=685×7=6835
The probability that the red ball came from Bag II is 6835.
Using Bayes’ theorem, we update the prior probability of choosing Bag II (1/2) with the likelihood of drawing a red ball from it (5/11), normalised by the total probability of drawing a red ball from either bag. The required probability is 6835.
The problem asks: given that a red ball was drawn, what is the chance it came from Bag II? This is a classic case of inverse probability — we know the outcome (red ball) and want the probability of a particular cause (Bag II). The natural tool is Bayes’ theorem, which reverses the conditional probability.
Why Bayes’ theorem works here:
We have two mutually exclusive and exhaustive events (choosing Bag I or Bag II), each with a prior probability of 1/2 (since the bag is chosen at random). For each bag, we know the probability of drawing a red ball (the “likelihood”). Bayes’ theorem combines these to give the posterior probability — the probability of the cause given the observed effect.
Let’s define the events clearly:
- B1: the ball is drawn from Bag I
- B2: the ball is drawn from Bag II
- R: the ball drawn is red
We need P(B2∣R).
- Write down the prior probabilities. Since the bag is chosen at random,
P(B1)=21,P(B2)=21.
-
Find the likelihoods — probability of drawing a red ball from each bag.
- Bag I: 3 red + 4 black = 7 balls total. So P(R∣B1)=73.
- Bag II: 5 red + 6 black = 11 balls total. So P(R∣B2)=115.
-
Compute the total probability of drawing a red ball (the denominator in Bayes’ theorem).
By the law of total probability:
P(R)=P(R∣B1)P(B1)+P(R∣B2)P(B2)
P(R)=73⋅21+115⋅21=143+225.
Find a common denominator (154):
143=15433,225=15435.
So
P(R)=15433+35=15468=7734.
You can keep P(R) as 15468 and simplify later — sometimes it’s easier to avoid reducing until the final step, especially when the numerator and denominator in Bayes’ formula share factors.
- Apply Bayes’ theorem.
P(B2∣R)=P(R)P(R∣B2)P(B2)=7734115⋅21.
Simplify step by step:
Numerator: 115⋅21=225.
So
P(B2∣R)=34/775/22=225×3477=22×345×77.
Cancel common factors: 77=7×11, 22=2×11. The 11 cancels:
2×345×7=6835.
A common mistake is to forget that the denominator P(R) must include contributions from both bags. If you only use the likelihood from Bag II, you’ll get 115 — which is the probability of red given Bag II, not the probability that the red came from Bag II.
- Interpret the result. The probability 6835≈0.5147 is slightly more than 1/2. This makes sense: Bag II has a higher proportion of red balls (5/11≈0.455) than Bag I (3/7≈0.429), so observing a red ball shifts the probability slightly in favour of Bag II.
The probability that the red ball was drawn from Bag II is 6835.
Method: Bayes' Theorem (finding the cause from the observed outcome)
Use this whenever you observe an outcome and are asked which source most likely produced it — the conditioning is reversed from what the problem hands you (you know the chance of a red ball given each bag, but want the chance of a bag given a red ball).
Steps
Step 1: Set up the partition of possible causes.
List the mutually exclusive, exhaustive hypotheses (here: the ball came from Bag I or Bag II) 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: drawing a red ball) 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 for bag/urn problems: the likelihood of the observed colour from each source is computed against that source's own total (Bag I has 3+4=7 balls, Bag II has 5+6=11), so do not compare raw colour counts as if the totals were equal.
Common Mistakes
Mistake 1: Reporting P(red∣Bag II)=115 as the answer.
Why it's wrong: that is the likelihood (red given Bag II), not the required posterior P(Bag II∣red). Correct approach: apply Bayes' theorem to reverse the conditioning.
Mistake 2: Leaving Bag I out of the denominator.
Why it's wrong: the total probability of drawing a red ball must include contributions from both bags. Correct approach: denominator =P(B1)P(R∣B1)+P(B2)P(R∣B2).
Mistake 3: Comparing raw red-ball counts (5 vs 3).
Why it's wrong: the bags have different totals (7 vs 11), so counts are not comparable. Correct approach: use probabilities 73 and 115, each over its own bag's total.
Showing the 12 most recent of 69 on this concept.
- AP EAPCET 2024Set eng-2024-05-23-FN1 markMCQQ.Bag A contains 2 white and 3 red balls and bag B contains 4 white and 5 red balls. If one ball is drawn at random from one of the bags and is found to be red, then the probability that it was drawn from the bag B is (A) 5423 (B) 5125 (C) 5225 (D) 5527
›Reveal solutionSolution
A classic Bayes'-theorem problem: given the ball drawn is red, the probability it came from bag B is 5225.
Concept and Intuition
This is Bayes' theorem: we're given the outcome (red ball drawn) and want to find the probability of which "cause" (bag A or B) produced it, using the prior probability of choosing each bag (each 21) and each bag's own probability of yielding red.
Step-by-Step Solution
- Bag A: 2 white, 3 red (5 total) ⇒P(red∣A)=53.
- Bag B: 4 white, 5 red (9 total) ⇒P(red∣B)=95.
- P(A)=P(B)=21 (bag chosen at random).
- Total probability of red: P(red)=P(A)P(red∣A)+P(B)P(red∣B)=21⋅53+21⋅95=103+185.
- Common denominator 90: 103=9027, 185=9025, sum =9052=4526.
- Bayes: P(B∣red)=P(red)P(B)P(red∣B)=26/45(1/2)(5/9)=26/455/18.
- =185×2645=18×265×45=468225.
- Simplify by dividing numerator and denominator by 9: 468225=5225.
Common Mistakes
- Forgetting to divide by the total probability P(red) (just stopping at P(B)P(red∣B)) — that omits the normalization Bayes' theorem requires.
- Arithmetic slip finding the common denominator for 3/10 and 5/18.
✓Final answerThe correct option is (C) — 5225.
ANSWER: C
- AP EAPCET 2024Set eng-2024-05-19-AN1 markMCQQ.There are 2 bags each containing 3 white and 5 black balls and 4 bags each containing 6 white and 4 black balls. If a ball drawn randomly from a bag is found to be black, then the probability that this ball is from the first set of bags is (A) 5725 (B) 4125 (C) 52 (D) 53
›Reveal solutionSolution
A classic Bayes'-theorem (inverse-probability) question: given the ball drawn is black, find the probability it came from the first group of bags. Answer: 5725.
Concept and Intuition
When an experiment happens in two stages — first a bag is picked (from one of two groups of bags), then a ball is drawn from it — and we're told the result of the second stage (a black ball came out), Bayes' theorem lets us reverse the direction of reasoning and find the probability about the first stage (which group the bag was from). The prior probability of picking from a group is proportional to how many bags are in that group (each individual bag is equally likely to be picked), and then we weight by how likely that group is to produce the observed outcome.
Step-by-Step Solution
- Groups and priors. Group 1 (call it S1) has 2 bags (each 3 white, 5 black — 8 balls). Group 2 (S2) has 4 bags (each 6 white, 4 black — 10 balls). Total bags =6, each equally likely to be chosen, so
P(S1)=62=31,P(S2)=64=32.
- Likelihoods of drawing black. From a group-1 bag: P(B∣S1)=85. From a group-2 bag: P(B∣S2)=104=52.
- Total probability of drawing a black ball (law of total probability):
P(B)=P(S1)P(B∣S1)+P(S2)P(B∣S2)=31⋅85+32⋅52=245+154.
Using denominator 120: 245=12025, 154=12032, so P(B)=12057.
4. Bayes' theorem:
P(S1∣B)=P(B)P(S1)P(B∣S1)=57/12025/120=5725.
Common Mistakes
- Weighting the two groups by number of black balls total instead of number of bags — the bag is chosen first (uniformly among all 6 bags), the composition only matters once a bag is picked.
- Arithmetic slip converting 245 and 154 to a common denominator (LCM of 24 and 15 is 120, not their product).
✓Final answerThe correct option is (A) — 5725.
ANSWER: A
- AP EAPCET 2022Set eng-2022-07-05-FN1 markMCQQ.A bag contains 4 red and 3 black balls. A second bag contains 2 red and 3 black balls. One bag is selected at random. If from the selected bag, one ball is drawn at random, then the probability that the ball drawn is red is (A) 7039 (B) 7041 (C) 7029 (D) 3517
›Reveal solutionSolution
Total probability theorem over the two equally-likely bags gives 3517.
Concept and Intuition
Since the bag is chosen at random (each with probability 21), the overall probability of drawing red is the weighted average of the conditional probabilities of drawing red from each bag.
Step-by-Step Solution
- Bag 1: 4 red, 3 black (7 total) → P(red∣Bag1)=74.
- Bag 2: 2 red, 3 black (5 total) → P(red∣Bag2)=52.
- P(red)=21⋅74+21⋅52=144+102=72+51.
- Common denominator 35: 3510+357=3517.
Common Mistakes
- Averaging the counts of red balls across bags instead of averaging the conditional probabilities.
✓Final answerThe correct option is (D) — 3517.
ANSWER: D
- AP EAPCET 2025Set eng-2025-05-23-AN1 markMCQQ.A bag contains 5 balls of unknown colours. There are equal chances that out of these five balls, there may be 0 or 1 or 2 or 3 or 4 or 5 red balls. A ball is taken out from the bag at random and is found to be red. The probability that it is the only red ball in the bag is (A) 51 (B) 61 (C) 151 (D) 301
›Reveal solutionSolution
This is a Bayes'-theorem problem over the six equally likely compositions of red balls; the posterior probability that exactly one ball is red, given a red ball was drawn, is 1/15.
Concept and Intuition
Bayes' theorem updates the prior (uniform belief over how many red balls there are) using the evidence (a red ball was drawn) — compositions with more red balls make drawing red more likely, so they get more posterior weight, but we want specifically the R=1 case.
Step-by-Step Solution
- Prior: P(R=r)=61 for r=0,1,2,3,4,5.
- Likelihood of drawing red given R=r red balls among 5: P(red∣R=r)=5r.
- Total probability of drawing red: P(red)=∑r=0561⋅5r=301(0+1+2+3+4+5)=3015=21.
- Joint probability for R=1: P(R=1)⋅P(red∣R=1)=61⋅51=301.
- By Bayes' theorem: P(R=1∣red)=1/21/30=302=151.
Common Mistakes
- Forgetting to normalize by the total probability of drawing red (just reporting the joint probability 1/30 as the answer).
- Mis-computing ∑r=0+1+2+3+4+5=15.
✓Final answerThe correct option is (C) — 151.
ANSWER: C
- AP EAPCET 2023Set eng-2023-05-17-AN1 markMCQQ.Three boxes B1, B2 and B3 contain balls with different colors as follows:A die is thrown. Box B1 is chosen if either 1 or 2 turns up. Box B2 is chosen if 3 or 4 turns up and box B3 is chosen if 5 or 6 turns up. Having chosen a box in this way, a ball is drawn at random from that box. If the ball drawn is found to be Red, then the probability that it is drawn from box B2 is (A) 127 (B) 125 (C) 121 (D) 263
White Black Red B1 2 1 2 B2 3 2 4 B3 4 3 2 ›Reveal solutionSolution
A Bayes'-theorem problem: reverse the conditional probability "ball is Red, which box?" using the total-probability formula.
Concept and Intuition
Bayes' theorem lets us "invert" a conditional probability. We know P(Red∣Bi) for each box and the prior P(Bi) (each 1/3 since the die is fair and each box corresponds to two faces); we want the posterior P(B2∣Red).
Step-by-Step Solution
- Totals in each box: B1: 2+1+2=5; B2: 3+2+4=9; B3: 4+3+2=9.
- P(Red∣B1)=52, P(Red∣B2)=94, P(Red∣B3)=92.
- Each box has prior probability 31 (each box corresponds to two die faces out of six).
- Total probability: P(Red)=31(52+94+92)=31(52+32)=31⋅1516=4516.
- Bayes: P(B2∣Red)=P(Red)P(B2)P(Red∣B2)=451631⋅94=4516274=274×1645=125.
Common Mistakes
- Forgetting to weight each box's conditional probability by its prior 1/3.
- Arithmetic slip when adding the three fractions with different denominators (5 and 9).
✓Final answerThe correct option is (B) — 125.
ANSWER: B
- AP EAPCET 2024Set eng-2024-05-19-AN1 markMCQQ.A bag contains 2 white, 3 green and 5 red balls. If three balls are drawn one after the other without replacement, then the probability that the last ball drawn was red is (A) 32 (B) 43 (C) 95 (D) 21
›Reveal solutionSolution
By the symmetry of random sampling without replacement, the probability that the last ball drawn is red equals the overall fraction of red balls in the bag: 105=21.
Concept and Intuition
When balls are drawn one after another without replacement, every ball is equally likely to occupy any given position in the drawing order (all 10! orderings of the bag's balls are equally likely). Hence the marginal probability that the ball in any fixed position (first, second, ..., last) is red is just (number of red balls)/(total balls), independent of which position we pick.
Step-by-Step Solution
- Bag: 2 white +3 green +5 red =10 balls total.
- Consider a full random permutation of all 10 balls (drawing three is just looking at the first three positions of such a permutation, but the argument works for any position).
- By symmetry, P(ball in position k is red)=105 for every position k, including the third (last) position drawn here.
- Therefore P(last ball drawn is red)=105=21.
Common Mistakes
- Trying to expand this via a long case-by-case conditional probability tree (drawing red first/second/third in various orders) — correct, but unnecessarily long; the symmetry argument gives the same answer instantly.
- Assuming the last draw is somehow "different" because two balls were already removed — draws without replacement are exchangeable, so it isn't.
✓Final answerThe correct option is (D) — 21.
ANSWER: D
- AP EAPCET 2026Set eng-2026-05-18-FN1 markMCQQ.Two balls are drawn at random from a box containing 4 white, 6 black balls one after the other without replacement. If it is known that second ball drawn is black, then the probability that the first ball drawn is also black is (A) 115 (B) 95 (C) 125 (D) 135
›Reveal solutionSolution
Conditional probability with sampling without replacement, solved with Bayes' theorem; answer is 95.
Concept and Intuition
When balls are drawn one after another without replacement, the marginal probability that any particular draw (say the 2nd) is black equals the overall proportion of black balls, 106 — position doesn't matter for the marginal event by symmetry. To find the conditional probability of the first draw given information about the second, we use Bayes' theorem: we need the joint probability of both events and divide by the marginal probability of the conditioning event.
Step-by-Step Solution
- Total balls: 4 white (W) + 6 black (B) = 10.
- P(2nd is black)=P(1st B, 2nd B)+P(1st W, 2nd B) =106⋅95+104⋅96=9030+9024=9054=53.
- P(1st black and 2nd black)=106⋅95=9030=31.
- By Bayes' theorem: P(1st black∣2nd black)=P(2nd B)P(1st B, 2nd B)=3/51/3=95.
Common Mistakes
- Assuming P(2nd black)=95 (the conditional probability given 1st was black) instead of computing the correct marginal 53.
- Forgetting to include both cases (1st W/2nd B and 1st B/2nd B) when finding the joint denominator event.
✓Final answerThe correct option is (B) — 95.
ANSWER: B
- AP EAPCET 2026Set eng-2026-05-13-FN1 markMCQQ.Bag A contains 3 white and 4 black balls. Bag B contains 4 white and 3 black balls. Bag C contains 2 white and 5 black balls. A bag is randomly selected and then a ball is randomly drawn from that bag. If the ball drawn was found to be white, then the probability that the ball is drawn from bag C is (A) 61 (B) 92 (C) 41 (D) 132
›Reveal solutionSolution
This is a direct Bayes'-theorem (inverse probability) question. Answer: 92.
Concept and Intuition
We're given the outcome (a white ball was drawn) and asked for the probability of a particular cause (it came from bag C). This is exactly Bayes' theorem: P(C∣W)=∑iP(bagi)P(W∣bagi)P(C)P(W∣C).
Step-by-Step Solution
- Each bag is chosen with probability 31.
- P(W∣A)=73 (3 white out of 7 total in bag A).
- P(W∣B)=74 (4 white out of 7 in bag B).
- P(W∣C)=72 (2 white out of 7 in bag C).
- Total probability of white: P(W)=31(73+74+72)=31⋅79=219=73.
- By Bayes' theorem: P(C∣W)=7331⋅72=73212=212×37=6314=92.
Common Mistakes
- Forgetting to divide by the total probability P(W) and stopping at the numerator 212.
- Mixing up which bag's white-ball fraction goes with which term.
✓Final answerThe correct option is (B) — 92.
ANSWER: B
- AP EAPCET 2026Set eng-2026-05-15-AN1 markMCQQ.A bag P contains 5 white and 4 blue balls. Another bag Q contains 4 white and 5 blue balls. A bag is randomly selected and a ball is drawn at random. If the ball selected from that bag is transferred to another bag, then the probability that bag Q has same number of blue and white balls is (A) 2/9 (B) 1/9 (C) 5/9 (D) 4/9
›Reveal solutionSolution
This tests conditional probability with a transfer-between-bags setup; you must track which single transfer restores balance to bag Q, in both directions of transfer. Answer: 5/9.
Concept and Intuition
A ball moves from whichever bag is picked into the other bag. "Bag Q ends up balanced" can happen two structurally different ways: either Q gains a ball (if P was picked) or Q loses a ball (if Q was picked). Each scenario needs the transferred ball to be a specific colour for Q's counts to equalize, so we compute each scenario's probability and add them (mutually exclusive cases, law of total probability).
Step-by-Step Solution
- Bag P: 5W,4B (9 total). Bag Q: 4W,5B (9 total).
- Scenario A (P picked, prob 1/2): a ball moves P→Q, so Q becomes a 10-ball bag. Q needs to end at 5W,5B. Since Q started 4W,5B, the incoming ball must be white. P(white∣P)=5/9. Probability of this scenario succeeding: 21×95=185.
- Scenario B (Q picked, prob 1/2): a ball moves Q→P, so Q becomes an 8-ball bag. Q started 4W,5B; removing a blue ball leaves 4W,4B — balanced. P(blue∣Q)=5/9. Probability: 21×95=185.
- Add the two mutually exclusive scenarios: 185+185=1810=95.
Common Mistakes
- Forgetting that Q's total ball count changes (9→10 or 9→8) after the transfer, so "equal white and blue" means different target counts in each scenario.
- Only considering the P→Q direction and missing the Q→P case.
✓Final answerThe correct option is (C) — 5/9.
ANSWER: C
- AP EAPCET 2026Set eng-2026-05-12-AN1 markMCQQ.A bag 'A' contains 2 black and 3 white balls. Another bag 'B' contains 3 black and 2 white balls. Two balls are drawn randomly from 'A' and placed in 'B'. Later, if two balls are drawn randomly from 'B', then the probability of getting a black ball and a white ball from it is (A) 10559 (B) 10546 (C) 21059 (D) 21067
›Reveal solutionSolution
A two-stage random-transfer problem — condition on what got transferred from A to B, then apply the law of total probability.
Concept and Intuition
Since the composition of bag B after the transfer depends on which 2 balls were drawn from A, we must split into the three possible transfer outcomes (BB, BW, WW), compute the probability of each, then the conditional probability of drawing one black and one white ball from the resulting bag B, and combine via the law of total probability.
Step-by-Step Solution
- Bag A has 5 balls (2B, 3W); ways to choose 2: (25)=10.
- P(BB from A)=(22)/(25)=1/10.
- P(BW from A)=(12)(13)/(25)=6/10=3/5.
- P(WW from A)=(23)/(25)=3/10.
- Bag B originally has 3B, 2W (5 balls); after adding 2 balls it has 7 balls, and we want P(1B,1W) drawn from it: (27)=21 ways.
- If BB added: B = 5B, 2W. P(1B,1W)=(15)(12)/21=10/21.
- If BW added: B = 4B, 3W. P(1B,1W)=(14)(13)/21=12/21.
- If WW added: B = 3B, 4W. P(1B,1W)=(13)(14)/21=12/21.
- Total probability:
P=101⋅2110+53⋅2112+103⋅2112
- Convert each term to a denominator of 210: 21010+21072+21036=210118=10559.
Common Mistakes
- Forgetting that bag B's composition changes with the transfer outcome, and just using the original bag B composition.
- Arithmetic slip when combining fractions with different denominators — always reduce to a common denominator before adding.
✓Final answerThe correct option is (A) — 10559.
ANSWER: A
- Bag A has 5 balls (2B, 3W); ways to choose 2: (25)=10.
- AP EAPCET 2026Set eng-2026-05-18-FN1 markMCQQ.A box P contains 3 white and 7 red balls. A bag Q contains 4 green and 5 blue balls. Two balls are randomly drawn from box P. If both are of same color, one ball is drawn from bag Q and if the two balls are of different color, 2 balls are drawn from the bag Q. If it is known that there is exactly one green ball among the ball or balls drawn from bag Q, then the probability that the two balls drawn from box P are of different colors is (A) 6735 (B) 6221 (C) 4320 (D) 6732
›Reveal solutionSolution
A two-stage Bayes' theorem problem; conditioning on "exactly one green ball from Q" gives P(different colors from P)=6735.
Concept and Intuition
This is a compound experiment: the outcome in box P (same-color vs different-color) determines how many balls are drawn from bag Q, and hence changes the probability model for "exactly one green." We must compute, for each P-outcome, the probability of observing exactly one green from Q, then combine via Bayes' theorem using the P-outcome's prior probability.
Step-by-Step Solution
- Box P has 3 white + 7 red = 10 balls. P(same color)=10C23C2+7C2=453+21=4524=158. P(different colors)=1−158=157 (check: 10C23C17C1=4521=157 ✓).
- If same color (S): draw 1 ball from Q (4 green, 5 blue, 9 total). "Exactly one green" among 1 ball drawn just means that ball is green: P(1 green∣S)=94.
- If different colors (D): draw 2 balls from Q. P(exactly one green∣D)=9C24C1⋅5C1=3620=95.
- By Bayes' theorem:
P(D∣one green)=P(S)P(one green∣S)+P(D)P(one green∣D)P(D)P(one green∣D)=158⋅94+157⋅95157⋅95
- Numerator =13535; denominator =13532+13535=13567. So P(D∣one green)=6735.
Common Mistakes
- Using the same probability model for "one green" regardless of whether 1 or 2 balls were drawn from Q — the two cases have genuinely different sample spaces.
- Forgetting to weight each conditional probability by the correct prior P(S) or P(D) before combining.
✓Final answerThe correct option is (A) — 6735.
ANSWER: A
- AP EAPCET 2024Set eng-2024-05-22-FN1 markMCQQ.A bag contains 6 balls. If three balls are drawn at a time and all of them are found to be green, then the probability that exactly 5 of the balls in the bag are green is: (A) 354 (B) 355 (C) 72 (D) 71
›Reveal solutionSolution
This is a Bayes'-theorem problem: given a uniform prior on the unknown number of green balls in the bag, update the belief after observing 3 green balls drawn. Answer: 2/7.
Concept and Intuition
Before drawing, any number of green balls from 0 to 6 is equally likely (7 equally likely hypotheses). Observing "all 3 drawn balls are green" is much more likely under hypotheses with more green balls, so Bayes' theorem reweights the prior toward higher k. We want the posterior probability that k=5.
Step-by-Step Solution
- Prior: P(k green balls)=71 for k=0,1,…,6.
- Likelihood of drawing 3 balls, all green, given k green balls out of 6: P(all green∣k)=(36)(3k)=20(3k) (zero for k<3).
- Compute: k=3:(33)=1⇒1/20; k=4:(34)=4⇒4/20; k=5:(35)=10⇒10/20; k=6:(36)=20⇒20/20=1.
- Total probability of observing "all green" (law of total probability, prior cancels as common factor 1/7): proportional to 1/20+4/20+10/20+20/20=35/20.
- Posterior P(k=5∣all green)=35/2010/20=3510=72.
Common Mistakes
- Forgetting that k=0,1,2 contribute zero likelihood (can't draw 3 green balls if fewer than 3 exist).
- Confusing this posterior-probability question with a plain hypergeometric-probability question (i.e., computing P(all green∣k=5) instead of P(k=5∣all green)).
✓Final answerThe correct option is (C) — 72.
ANSWER: C
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