Exercise 14.2 answers: Probability

Class 11 Maths21 questions

Exercise 14.2

21 questions · page 305 of the book

Question 1

“Which of the following can not be valid assignment of probabilities for outcomes of sample Space” · p. 305

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(a)

  1. Add the seven numbers: 0.1+0.01+0.05+0.03+0.01+0.2+0.6 = 1.
  2. Every value is between 0 and 1, and the total is exactly 1.

AnswerValid — non-negative and adds to 1.

(b)

  1. Each of the seven outcomes gets 1/7.
  2. Seven sevenths add to exactly 1, and each value is between 0 and 1.

AnswerValid — an equal split of probability among all seven outcomes.

(c)

  1. Add the seven values: 0.1+0.2+0.3+0.4+0.5+0.6+0.7 = 2.8.

AnswerNot valid — the probabilities add up to 2.8, not 1.

(d)

  1. Two of the values, −0.1 and −0.2, are negative.

AnswerNot valid — a probability can never be negative.

(e)

  1. Add the seven values: (1+2+3+4+5+6+15)/14 = 36/14, which is more than 1.
  2. The last value, 15/14, is itself bigger than 1.

AnswerNot valid — the probabilities add to 36/14, which is more than 1.

Watch this explained “Five candidates, tested”, 15:12 into Three conditions on a function, in place of a recipe for counting

Question 2

“A coin is tossed twice, what is the probability that atleast one tail occurs?” · p. 306

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  1. List all outcomes of tossing a coin twice: HH, HT, TH, TT, each equally likely.
  2. ‘At least one tail’ fails only for HH, the one outcome with no tail at all.
  3. So P(at least one tail) = 1 − P(HH) = 1 − 1/4.

Answer3/4

Watch this explained “At least one”, 9:36 into Knowing one probability hands you its opposite for free

Question 3

“A die is thrown, find the probability of following events” · p. 306

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(i) A prime number will appear

  1. The prime faces on a die are 2, 3 and 5 — 3 outcomes out of 6.

Answer1/2

(ii) A number greater than or equal to 3

  1. 3, 4, 5 and 6 qualify — 4 outcomes out of 6.

Answer2/3

(iii) A number less than or equal to one

  1. Only 1 qualifies — 1 outcome out of 6.

Answer1/6

(iv) A number more than 6 will appear

  1. No face on a die is more than 6.

Answer0

(v) A number less than 6 will appear

  1. 1, 2, 3, 4 and 5 qualify — 5 outcomes out of 6.

Answer5/6

Watch this explained “The rule comes back”, 7:40 into Assuming the outcomes are equally likely recovers the counting rule

Question 4

“A card is selected from a pack of 52 cards” · p. 306

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(a) How many points are there in the sample space?

  1. A pack has 52 different cards, each equally likely to be drawn.

Answer52

(b) the card is an ace of spades

  1. Only one card is exactly the ace of spades, out of 52.

Answer1/52

(c)(i) an ace

  1. There are 4 aces in the pack, one per suit.

Answer4/52 = 1/13

(c)(ii) black card

  1. Half the pack is black — 26 clubs and spades.

Answer26/52 = 1/2

Watch this explained “Where the hypothesis is safe”, 8:37 into Assuming the outcomes are equally likely recovers the counting rule

Question 5

“A fair coin with 1 marked on one face and 6 on the other” · p. 306

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(i) 3

  1. The coin shows 1 or 6, and the die shows 1 to 6, giving 12 equally likely pairs.
  2. A sum of 3 only happens as (coin = 1, die = 2).

Answer1/12

(ii) 12

  1. A sum of 12 only happens as (coin = 6, die = 6).

Answer1/12

Watch this explained “The rule comes back”, 7:40 into Assuming the outcomes are equally likely recovers the counting rule

Question 6

“There are four men and six women on the city council” · p. 306

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  1. The council has 4 + 6 = 10 members in total.
  2. 6 of these 10 members are women.

Answer6/10 = 3/5

Watch this explained “Where the hypothesis is safe”, 8:37 into Assuming the outcomes are equally likely recovers the counting rule

Question 7

“a person win Re 1 for each head and lose Rs 1.50 for each tail that turns up” · p. 306

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  1. There are 2⁴ = 16 equally likely sequences of 4 tosses.
  2. With h heads (and 4−h tails), the money is h × ₹1 − (4−h) × ₹1.50.
  3. Working this out for h = 0, 1, 2, 3, 4 gives −₹6, −₹3.50, −₹1, ₹1.50 and ₹4 — five different amounts.
  4. The number of sequences giving h heads is ⁴Cₕ: 1, 4, 6, 4, 1 out of 16.
  5. So, in the same order as the amounts, the probabilities are 1/16, 1/4, 3/8, 1/4 and 1/16.

Answer5 different amounts: −₹6, −₹3.50, −₹1, ₹1.50, ₹4, with probabilities 1/16, 1/4, 3/8, 1/4, 1/16.

Watch this explained “The list and the values are different things”, 13:03 into Assuming the outcomes are equally likely recovers the counting rule

Question 8

“Three coins are tossed once. Find the probability of getting” · p. 306

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(i) 3 heads

  1. Only HHH gives 3 heads — 1 outcome out of 8.

Answer1/8

(ii) 2 heads

  1. HHT, HTH and THH give exactly 2 heads — 3 outcomes.

Answer3/8

(iii) atleast 2 heads

  1. Add the 3 outcomes with 2 heads to the 1 outcome with 3 heads — 4 outcomes.

Answer1/2

(iv) atmost 2 heads

  1. Every outcome except HHH has at most 2 heads — 7 outcomes.

Answer7/8

(v) no head

  1. Only TTT has no head.

Answer1/8

(vi) 3 tails

  1. Only TTT has 3 tails — the same single outcome as ‘no head’.

Answer1/8

(vii) exactly two tails

  1. Exactly 2 tails means exactly 1 head — HTT, THT, TTH — 3 outcomes.

Answer3/8

(viii) no tail

  1. Only HHH has no tail.

Answer1/8

(ix) atmost two tails

  1. Every outcome except TTT has at most 2 tails — 7 outcomes.

Answer7/8

Watch this explained “At least one”, 9:36 into Knowing one probability hands you its opposite for free

Question 9

“what is the probability of the event ‘not A’” · p. 306

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  1. For any event, P(A) + P(not A) = 1.
  2. So P(not A) = 1 − 2/11.

Answer9/11

Watch this explained “The proof, in one line”, 4:03 into Knowing one probability hands you its opposite for free

Question 10

“A letter is chosen at random from the word ‘ASSASSINATION’” · p. 306

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(i) a vowel

  1. The word ASSASSINATION has 13 letters in all.
  2. The vowels among them are A (×3), I (×2) and O (×1) — 6 vowels out of 13.

Answer6/13

(ii) a consonant

  1. The consonants are S (×4), N (×2) and T (×1) — 7 out of 13.

Answer7/13

Watch this explained “A die with repeated faces”, 14:07 into Assuming the outcomes are equally likely recovers the counting rule

Question 11

“In a lottery, a person choses six different natural numbers at random from 1 to 20” · p. 307

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  1. The person chooses 6 different numbers from the numbers 1 to 20.
  2. Since order does not matter, the total number of ways to choose 6 numbers from 20 is C(20,6).
  3. C(20,6) = 20!/(6! × 14!) = (20 × 19 × 18 × 17 × 16 × 15)/(6 × 5 × 4 × 3 × 2 × 1)
  4. C(20,6) = 27907200/720 = 38760
  5. There is only 1 way to choose the exact 6 numbers fixed by the committee.
  6. P(winning) = 1/38760

Answer1/38760

Watch this explained “Seven cards from fifty-two”, 11:06 into Assuming the outcomes are equally likely recovers the counting rule

Question 12

“Check whether the following probabilities P(A) and P(B) are consistently defined” · p. 307

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(i) P(A) = 0.5, P(B) = 0.7, P(A ∩ B) = 0.6

  1. A ∩ B is part of both A and B, so P(A ∩ B) can never be more than the smaller of P(A) and P(B).
  2. Here P(A ∩ B) = 0.6 is bigger than P(A) = 0.5 — that is impossible.

AnswerNot consistently defined.

(ii) P(A) = 0.5, P(B) = 0.4, P(A ∪ B) = 0.8

  1. Using P(A ∪ B) = P(A) + P(B) − P(A ∩ B), the implied overlap is 0.5 + 0.4 − 0.8 = 0.1.
  2. 0.1 lies between 0 and the smaller of 0.5 and 0.4, so this is possible.

AnswerConsistently defined.

Watch this explained “Can any three numbers do this job?”, 14:13 into Adding two probabilities double-counts the overlap, so the overlap comes back off

Question 13

“Fill in the blanks in following table” · p. 307

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(i)

  1. Use P(A∪B) = P(A) + P(B) − P(A∩B).
  2. P(A) = 1/3, P(B) = 1/5, P(A∩B) = 1/15.
  3. P(A∪B) = 1/3 + 1/5 − 1/15 = 5/15 + 3/15 − 1/15 = 7/15.

Answer7/15

(ii)

  1. Rearrange the same rule: P(B) = P(A∪B) − P(A) + P(A∩B).
  2. P(A∪B) = 0.6, P(A) = 0.35, P(A∩B) = 0.25.
  3. P(B) = 0.6 − 0.35 + 0.25 = 0.5.

Answer1/2

(iii)

  1. Rearrange the rule: P(A∩B) = P(A) + P(B) − P(A∪B).
  2. P(A) = 0.5, P(B) = 0.35, P(A∪B) = 0.7.
  3. P(A∩B) = 0.5 + 0.35 − 0.7 = 0.15.

Answer3/20

Watch this explained “The rule, run backwards”, 16:47 into Adding two probabilities double-counts the overlap, so the overlap comes back off

Question 14

“Find P(A or B), if A and B are mutually exclusive events” · p. 307

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  1. A and B are mutually exclusive, so they share no outcome.
  2. For mutually exclusive events, P(A or B) = P(A) + P(B).
  3. P(A or B) = 3/5 + 1/5 = 4/5.

Answer4/5

Watch this explained “No overlap, and the rule is the axiom”, 11:56 into Adding two probabilities double-counts the overlap, so the overlap comes back off

Question 15

“find (i) P(E or F), (ii) P(not E and not F)” · p. 307

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(i) P(E or F)

  1. Use the addition rule: P(E or F) = P(E) + P(F) − P(E and F).
  2. P(E or F) = 1/4 + 1/2 − 1/8 = 2/8 + 4/8 − 1/8 = 5/8.

Answer5/8

(ii) P(not E and not F)

  1. "Not E and not F" is the complement of "E or F".
  2. P(not E and not F) = 1 − P(E or F) = 1 − 5/8 = 3/8.

Answer3/8

Watch this explained “The rule, run backwards”, 16:47 into Adding two probabilities double-counts the overlap, so the overlap comes back off

Question 16

“P(not E or not F) = 0.25, State whether E and F are mutually exclusive” · p. 307

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  1. "Not E or not F" is the complement of "E and F" (De Morgan's law).
  2. So P(E and F) = 1 − P(not E or not F) = 1 − 0.25 = 0.75.
  3. E and F are mutually exclusive only if P(E and F) = 0.
  4. Since P(E and F) = 0.75, which is not 0, E and F are not mutually exclusive.

AnswerNo

Watch this explained “Four more wordings”, 11:46 into Knowing one probability hands you its opposite for free

Question 17

“Determine (i) P(not A), (ii) P(not B) and (iii) P(A or B)” · p. 307

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(i) P(not A)

  1. P(not A) = 1 − P(A) = 1 − 0.42 = 0.58.

Answer29/50

(ii) P(not B)

  1. P(not B) = 1 − P(B) = 1 − 0.48 = 0.52.

Answer13/25

(iii) P(A or B)

  1. Use the addition rule: P(A or B) = P(A) + P(B) − P(A and B).
  2. P(A or B) = 0.42 + 0.48 − 0.16 = 0.74.

Answer37/50

Watch this explained “The rule, run backwards”, 16:47 into Adding two probabilities double-counts the overlap, so the overlap comes back off

Question 18

“find the probability that he will be studying Mathematics or Biology” · p. 307

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  1. Let M = studies Mathematics, B = studies Biology.
  2. P(M) = 40/100, P(B) = 30/100, P(M and B) = 10/100.
  3. Use the addition rule: P(M or B) = P(M) + P(B) − P(M and B).
  4. P(M or B) = 0.4 + 0.3 − 0.1 = 0.6 = 3/5.

Answer3/5

Watch this explained “Two candidates, one entrance test”, 12:50 into Adding two probabilities double-counts the overlap, so the overlap comes back off

Question 19

“What is the probability of passing both?” · p. 307

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  1. Let A = passes first exam, B = passes second exam.
  2. P(A) = 0.8, P(B) = 0.7, P(A or B) = 0.95.
  3. Use the addition rule: P(A and B) = P(A) + P(B) − P(A or B).
  4. P(A and B) = 0.8 + 0.7 − 0.95 = 0.55 = 11/20.

Answer11/20

Watch this explained “The rule, run backwards”, 16:47 into Adding two probabilities double-counts the overlap, so the overlap comes back off

Question 20

“what is the probability of passing the Hindi examination?” · p. 307

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  1. Let E = passes English, H = passes Hindi.
  2. P(E and H) = 0.5, P(passes neither) = 0.1, so P(E or H) = 1 − 0.1 = 0.9.
  3. P(E) = 0.75.
  4. Use the addition rule: P(H) = P(E or H) − P(E) + P(E and H).
  5. P(H) = 0.9 − 0.75 + 0.5 = 0.65 = 13/20.

Answer13/20

Watch this explained “The rule, run backwards”, 16:47 into Adding two probabilities double-counts the overlap, so the overlap comes back off

Question 21

“30 opted for NCC, 32 opted for NSS and 24 opted for both NCC and NSS” · p. 308

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(i) The student opted for NCC or NSS

  1. P(NCC) = 30/60, P(NSS) = 32/60, P(both) = 24/60.
  2. P(NCC or NSS) = 30/60 + 32/60 − 24/60 = 38/60 = 19/30.

Answer19/30

(ii) opted neither NCC nor NSS

  1. "Neither" is the complement of "NCC or NSS".
  2. P(neither) = 1 − 19/30 = 11/30.

Answer11/30

(iii) opted NSS but not NCC

  1. "NSS but not NCC" removes the students who did both from the NSS group.
  2. P(NSS but not NCC) = P(NSS) − P(both) = 32/60 − 24/60 = 8/60 = 2/15.

Answer2/15

Watch this explained “Where the overlap has to be spotted”, 18:12 into Adding two probabilities double-counts the overlap, so the overlap comes back off

Every question here was solved twice, separately, by two different AI models, and each answer was put back into the question by a computer program to check it. Where the two disagreed, a stronger model solved it again and the computer check had to pass on its answer. A question about reasoning rather than a number is shown as “one way to think about it”, and anything not yet proven says so instead of guessing. Each answer links to the moment in the video that teaches it.

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