Q.(a) A die with numbers 1 to 6 is biased such that P(2)=103 and the probability of other numbers is equal. Find the mean of the number of times the number 2 appears on the die, if the die is thrown twice.
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🔒 Start your 14-day free trial to unlock the full solution →Part (a)Concept understanding — Binomial Distribution
Binomial Distribution: From Intuition to Precision
Imagine you flip a fair coin 10 times. You want to know the probability of getting exactly 6 heads. This is the kind of question the binomial distribution answers — it models the number of "successes" in a fixed number of independent trials, where each trial has only two outcomes.
The Intuition
Think of a single trial. You roll a die and call "getting a 4" a success. That's one trial. Now roll the die 5 times. The number of times you get a 4 could be 0, 1, 2, 3, 4, or 5. Each roll is independent — the result of one roll doesn't affect the next. The probability of success (getting a 4) stays the same every time: p=61.
The binomial distribution tells you exactly how likely each possible count of successes is, given:
- a fixed number of trials n,
- a constant success probability p per trial,
- independent trials.
The Key Conditions
For a situation to be modelled by a binomial distribution, all four must hold:
- Fixed number of trials (n). You decide in advance how many times you'll repeat the experiment.
- Two outcomes per trial — "success" and "failure". These are just labels; success is whatever outcome you're counting.
- Constant probability of success (p) on every trial.
- Independent trials. The outcome of one trial does not influence another.
A common mistake is to apply the binomial distribution when trials are not independent — for example, drawing cards without replacement from a deck. That's a hypergeometric situation, not binomial.
The Precise Statement
Let X be the number of successes in n independent trials, each with success probability p. Then X follows a binomial distribution with parameters n and p, written:
X∼Binomial(n,p)
The probability of getting exactly k successes (where k=0,1,2,…,n) is:
P(X=k)=(kn)pk(1−p)n−k
P(X=k)=(kn)pk(1−p)n−k
Breaking Down the Formula
Three pieces multiply together:
- pk — the probability that the k successes happen. Since each success has probability p, and there are k of them, this factor is p multiplied by itself k times.
- (1−p)n−k — the probability that the remaining n−k trials are failures. Each failure has probability 1−p.
- (kn) — the number of ways to choose which k of the n trials are the successes. The successes could be the first k trials, or the last k, or any arrangement. This binomial coefficient counts all possible arrangements.
The binomial coefficient (kn) is read as "n choose k" and equals k!(n−k)!n!. For example, (25)=2!3!5!=10.
A Worked Example
Problem: A multiple-choice test has 10 questions, each with 4 options. You guess every answer. What is the probability you get exactly 3 correct?
Solution:
- n=10 (10 trials)
- p=41 (probability of guessing correctly on one question)
- k=3 (we want exactly 3 successes)
P(X=3)=(310)(41)3(43)7
Compute (310)=120, so:
P(X=3)=120×641×(43)7
(43)7=163842187, so:
P(X=3)=120×641×163842187=64×16384120×2187
Simplify: 120/64=15/8, so:
P(X=3)=8×1638415×2187=13107232805≈0.2503
So there's about a 25% chance of getting exactly 3 correct by pure guessing.
Mean and Variance
For a binomial distribution, two important summary measures are: …
Part (b)Concept understanding — Event Independence
Event Independence
Two events are independent when the occurrence of one does not change the probability of the other. Toss a coin and roll a die: the coin landing heads tells you nothing about whether the die shows a six. Contrast this with drawing cards without replacement, where the first draw does change the odds for the second — those events are dependent.
From Conditional Probability to a Clean Test
"Knowing B doesn't change A" means P(A∣B)=P(A). Substituting the definition P(A∣B)=P(B)P(A∩B) and clearing the fraction gives the symmetric form used in practice:
P(A∩B)=P(A)P(B).
Events A and B are independent exactly when the probability of both occurring equals the product of their individual probabilities. This version is preferred because it needs no non-zero condition and treats A and B alike.
A Quick Check
Roll a fair die. Let A={2,4,6} (even) and B={4,5,6} (greater than 3). Then P(A)=P(B)=21, and A∩B={4,6} so P(A∩B)=31. Since 31=21⋅21=41, these events are not independent.
Three or More Events
Events A,B,C are mutually independent only if all four conditions hold: the three pairwise products and
P(A∩B∩C)=P(A)P(B)P(C).
Pairwise independence alone is not enough to guarantee mutual independence. …
Part (a)
The number of 2's in two independent throws is X∼Binomial(n=2, p=P(2)=103). (The other five faces share the remaining 107, but only p matters for the mean.) …
Part (a): the count of 2's in two throws is Binomial(2,103), mean =53. Part (b): P(A)P(B)=545=121=P(A∩B) and A∩B=∅, so A,B are neither independent nor mutually exclusive.
Part (a): mean number of 2's in two throws
We are told P(2)=103; the other five faces 1,3,4,5,6 are equally likely. If each has probability p, then 5p+103=1⇒p=507 — though for the mean we only need P(2).
Let X be the number of times 2 appears in two independent throws. Each throw is a Bernoulli trial with "success" = getting a 2, probability 103. Hence X∼Binomial(n=2, p=103).
For X∼Binomial(n,p), the mean is E(X)=np. …
Method: Mean of a count via np, and testing independence vs mutual exclusivity
Two techniques: getting a mean count without the full distribution, and classifying two events by the two defining equations.
Steps
Step 1 (mean): recognise a binomial count.
If you count how often a fixed-probability outcome occurs in n independent trials, the count is B(n,p) and its mean is E(X)=np — you need only p and n, not the whole distribution.
Step 2 (classification): compute three numbers.
From the sample space find P(A), P(B), and P(A∩B).
Step 3: Apply both tests independently.
- Mutually exclusive ⟺P(A∩B)=0. …
Common Mistakes
Mistake 1 (part a): Building the whole distribution when only the mean is asked.
Why it's wrong: the count of 2's in two throws is B(2,103), so E(X)=np=53 directly; you only need P(2), not the other faces' probabilities. Correct approach: use E(X)=np.
Mistake 2 (part b): Concluding "not mutually exclusive" therefore "independent" (or vice versa). …
Showing the 12 most recent of 82 on this concept.
- CBSE 2026Set 65/3/11 markMCQQ.If E and F are two independent events such that P(E)=103, P(E∪F)=21, then P(E∣F)−P(F∣E) is equal to: (A) 72 (B) 353 (C) 701 (D) 71
›Reveal solutionSolution
We use the property of independent events, P(E∩F)=P(E)P(F), along with the union formula to first find P(F). Then, we use the fact that for independent events, P(E∣F)=P(E) and P(F∣E)=P(F), to calculate the required difference. The final result is 701.
The core of this problem lies in understanding how the concept of "independent events" simplifies probability calculations, especially when dealing with unions and conditional probabilities.
When two events, E and F, are independent, it means that the occurrence of one event does not affect the probability of the other event occurring. This has two crucial implications:
- Intersection Probability: The probability of both E and F happening, P(E∩F), is simply the product of their individual probabilities: P(E∩F)=P(E)P(F).
- Conditional Probability: The probability of E happening given that F has already happened, P(E∣F), is just the probability of E, because F's occurrence doesn't change E's likelihood. So, P(E∣F)=P(E). Similarly, P(F∣E)=P(F).
We are given P(E), P(E∪F), and that E and F are independent. Our strategy will be to first use the formula for the union of events, combined with the independence property, to find P(F). Once we have P(F), we can directly use the independence property to find P(E∣F) and P(F∣E), and then calculate their difference.
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Find P(F) using the union formula and independence.
The general formula for the probability of the union of two events is:
P(E∪F)=P(E)+P(F)−P(E∩F)
Since E and F are independent, we can substitute P(E∩F) with P(E)P(F):
P(E∪F)=P(E)+P(F)−P(E)P(F)
Now, substitute the given values: P(E)=103 and P(E∪F)=21.
21=103+P(F)−103P(F)
To solve for P(F), group the terms involving P(F):
21=103+P(F)(1−103)
21=103+P(F)(107)
Subtract 103 from both sides:
21−103=P(F)(107)
To subtract the fractions on the left, find a common denominator, which is 10:
105−103=P(F)(107)
102=P(F)(107)
Now, isolate P(F) by multiplying both sides by 710: …
- CBSE 2026Set V11 markMCQQ.The probability of obtaining an even prime number on each die when a pair of dice is rolled(a) 361(b) 61(c) 181(d) 41
›Reveal solutionSolution
The even prime is 2; P(2 on each of two dice)=61⋅61=361; answer (a).
The only even prime number is 2. For one die, P(show 2)=61. The two dice are independent, so …
- CBSE 2026Set V11 markMCQQ.If A and B are independent events with P(A)=0.3 and P(B)=0.4 then P(A∩B)(a) 1.2(b) 0.12(c) 0.7(d) 43
›Reveal solutionSolution
Independence gives P(A∩B)=P(A)P(B)=0.12; answer (b).
For independent events A and B,
P(A∩B)=P(A)⋅P(B)=0.3×0.4=0.12. …
- CBSE 2026Set A1 markMCQQ.If A, B and C are three independent events then P(ABC)=(a) P(A)+P(B)+P(C)(b) P(A)−P(B)−P(C)(c) P(A)⋅P(B)⋅P(C)(d) None of these
›Reveal solutionSolution
For independent events, P(A∩B∩C)=P(A)P(B)P(C).
By definition, events A, B, C are (mutually) independent when the probability of their joint occurrence equals the product of their individual probabilities:
P(ABC)=P(A)⋅P(B)⋅P(C).
…
- CBSE 2026Set ANNUAL1 markMCQQ.In a box containing 100 bulbs, 10 are defective. Write the probability that out of a sample of 5 bulbs, none is defective.(a) 10−1(b) (21)5(c) (109)5(d) 109
›Reveal solutionSolution
Treating each draw as an independent trial with probability 109 of being non-defective, the probability that all 5 are non-defective is (109)5.
Out of 100 bulbs, 10 are defective, so the probability a randomly chosen bulb is not defective is
P(good)=100100−10=10090=109
…
- CBSE 2026Set ANNUAL1 markMCQQ.If A and B are independent events and P(A)=0.3 and P(B)=0.4, then the value of P(A∪B) will be(a) 0.58(b) 0.70(c) 0.12(d) 0.10
›Reveal solutionSolution
For independent events, P(A∩B)=P(A)P(B), and the addition rule gives P(A∪B).
P(A∩B)=0.3×0.4=0.12 (independence).
…
- CBSE 2026Set ANNUAL1 markMCQQ.The probability of obtaining an even prime number on each dice, when a pair of dice is rolled, is:(a) 0(b) 31(c) 121(d) 361
›Reveal solutionSolution
The only even prime number is 2, so we need a 2 on each die.
Among {1,2,3,4,5,6}, the only even prime is 2.
P(2 on one die)=61
…
- CBSE 2026Set ANNUAL1 markMCQQ.Ajay and Meera are contesting for two vacancies in a company. Probability of selection of Ajay is 7/9 and that of Meera is 4/7. What is the probability that both will be rejected?(a) 61/63(b) 6/63(c) 41/63(d) 28/63
›Reveal solutionSolution
The rejection probabilities of each candidate are complements of their selection probabilities; since the two events are independent, multiply them.
P(Ajay selected)=97⟹P(Ajay rejected)=1−97=92
P(Meera selected)=74⟹P(Meera rejected)=1−74=73
…
- CBSE 2026Set ANNUAL1 markMCQQ.Assertion (A): Two events A and B are such that P(A) = 1/4, P(B) = 1/2 and P(A∩B) = 1/8 then two events A and B are independent. Reason (R): Two events are independent if the probability of occurrence of one does not affect the probability of occurrence of other and P(A∩B) = P(A) + P(B) − P(A∪B)(a) Both Assertion (A) and Reason (R) are true and Reason (R) is the correct explanation of Assertion (A)(b) Both Assertion (A) and Reason (R) are true, but Reason (R) is not the correct explanation of Assertion (A)(c) Assertion (A) is true but Reason (R) is false.(d) Both Assertion (A) and Reason (R) are false.
›Reveal solutionSolution
The independence check in A is numerically correct, but R states the wrong criterion — it gives the addition-rule identity (true for ANY two events), not the actual independence condition P(A∩B)=P(A)⋅P(B).
Checking Assertion (A): Independence requires P(A∩B)=P(A)⋅P(B).
P(A)⋅P(B)=41×21=81,
which equals the given P(A∩B)=1/8. So A and B are independent — A is true.
Checking Reason (R): R correctly describes independence in words ("occurrence of one does not affect the other"), but then states the test as
P(A∩B)=P(A)+P(B)−P(A∪B). …
- CBSE 2026Set ANNUAL1 markMCQQ.Let E and F be events with P(E)=31, P(F)=21 and P(E∩F)=61. Then(a) E and F are independent events(b) E and F are mutually exclusive events(c) E and F are disjoint events(d) None of the above
›Reveal solutionSolution
Two events are independent exactly when P(E∩F)=P(E)⋅P(F); check whether the given numbers satisfy this.
Given P(E)=31, P(F)=21, P(E∩F)=61.
Test for independence:
P(E)⋅P(F)=31×21=61
This equals the given P(E∩F)=61. Since P(E∩F)=P(E)P(F), E and F are independent.
…
- CBSE 2026Set ANNUAL1 markQ.If A and B are two independent events with P(A) = 1/2 and P(B) = 1/3, then find P(A ∪ B).
›Reveal solutionSolution
For independent events, P(A∩B)=P(A)P(B); then apply the addition rule.
…
- CBSE 2026Set ANNUAL1 markQ.Write the answer in one word/sentence: If E and F are independent events then write the value of P(E∩F).
›Reveal solutionSolution
For independent events, the probability of the intersection is the product.
…
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