Q.A bag contains 4 white and 5 black balls. Another bag contains 9 white and 7 black balls. A ball is transferred from the first bag to the second and then a ball is drawn at random from the second bag. Find the probability that the ball drawn is white.
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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). …
The transferred ball can be white or black, so use the law of total probability: split on the two transfer cases and add the weighted probabilities of then drawing white.
1. Transfer from the first bag (4 white, 5 black, 9 total):
P(white transferred)=94,P(black transferred)=95.
2. Draw from the second bag (starts 9 white, 7 black; becomes 17 balls after the transfer):
- White transferred ⇒ 10 white, 7 black: P(white∣white transferred)=1710. …
Split on which ball was transferred and use the law of total probability; the probability the drawn ball is white is 95.
The idea
A ball is moved from the first bag into the second, and only then do we draw from the second bag. The trouble is we do not know which colour was transferred, and that colour changes what the second bag looks like. So we consider both possibilities, weight each by how likely that transfer was, and add the results. That is the law of total probability:
P(white)=P(white∣white transferred)P(white transferred)+P(white∣black transferred)P(black transferred).
Step 1 — the transfer
The first bag has 4 white and 5 black balls, 9 in all, so the transferred ball is just a random draw from it:
P(white transferred)=94,P(black transferred)=95.
Step 2 — drawing white from the second bag
The second bag starts with 9 white and 7 black balls (16 total). After one ball is added, it always holds 17 balls — this updated denominator is the step students most often miss.
- If a white ball was transferred, the second bag has 9+1=10 white and 7 black:
P(white∣white transferred)=1710.
- If a black ball was transferred, the second bag has 9 white and 7+1=8 black: …
Method: Law of Total Probability with a Changed Composition (transfer problems)
Use this whenever an item is moved from one collection to another and then something is drawn, so the second stage depends on an unknown first-stage outcome.
Steps
Step 1: Identify the hidden first stage and split it into cases.
The transferred item has an unknown identity. List every possibility (here: a white or a black ball moves), and find the probability of each straight from the source collection.
Step 2: For each case, update the second collection, then find the target conditional probability. …
Common Mistakes
Mistake 1: Keeping the second bag's total unchanged after the transfer.
Why it's wrong: one ball is added, so the total rises (here 16→17); using 16 in the denominators gives conditionals like 1610 that no longer describe the real bag. Correct approach: after any transfer, recount the total and use the updated denominator (17) in every conditional.
Mistake 2: Forgetting to weight the two cases by the transfer probability. …
Showing the 12 most recent of 69 on this concept.
- 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. …
- 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: …
- Bag A has 5 balls (2B, 3W); ways to choose 2: (25)=10.
- 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. …
- 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. …
- 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. …
- AP EAPCET 2023Set eng-2023-05-15-FN1 markMCQQ.Bag B1 contains 4 white and 2 black balls. Bag B2 contains 3 white and 4 black balls. A bag is chosen at random and a ball is drawn from it at random, then the probability that the ball drawn is white, is (A) 421 (B) 3242 (C) 4233 (D) 4223
›Reveal solutionSolution
Total-probability rule over the two equally likely bags gives P(white)=21⋅64+21⋅73=4223.
Concept and Intuition
The ball drawn depends on which bag was chosen first. Since the bag choice is random with P(B1)=P(B2)=21, and the draw is conditionally independent given the bag, the Law of Total Probability adds the two conditional probabilities weighted by how likely each bag is.
Step-by-Step Solution
- B1: 4 white, 2 black out of 6 ⇒P(white∣B1)=64=32.
- B2: 3 white, 4 black out of 7 ⇒P(white∣B2)=73.
- P(white)=21⋅32+21⋅73=31+143. …
- 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. …
- AP EAPCET 2025Set eng-2025-05-22-AN1 markMCQQ.An urn A contains 4 white and 1 black ball; urn B contains 3 white and 2 black balls and urn C contains 2 white and 3 black balls. One ball is transferred randomly from A to B; later one ball is transferred randomly from B to C. Finally, if a ball is drawn randomly from C, then the probability that it is a black ball is (A) 127 (B) 18089 (C) 180101 (D) 3617
›Reveal solutionSolution
A two-stage transfer-then-draw problem, solved by branching over all four possible transfer outcomes: probability of black =180101.
Concept and Intuition
This is a sequential conditional-probability problem: each transfer changes the composition of the receiving urn, so we must branch over every possible outcome of each transfer and weight the final draw accordingly (total probability theorem, applied twice).
Step-by-Step Solution
- Urn A: 4W,1B. Transfer to B: P(W)=54, P(B)=51.
- If white moved to B: B becomes 4W,2B (6 balls). If black moved to B: B becomes 3W,3B (6 balls).
- From B (4W,2B): transfer white to C with P=64=32 (C becomes 3W,3B), or black with P=62=31 (C becomes 2W,4B).
- From B (3W,3B): transfer white to C with P=21 (C becomes 3W,3B), or black with P=21 (C becomes 2W,4B).
- Final draw from C: P(black∣3W3B)=21; P(black∣2W4B)=64=32.
- Combine all four branches:
- A-white, B-white: 54⋅32⋅21=308=154
- A-white, B-black: 54⋅31⋅32=458 …
- 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. …
- 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: …
- 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. …
- 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. …
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