Q.(a) Two balls are drawn at random one by one with replacement from an urn containing equal number of red balls and green balls. Find the probability distribution of the number of red balls. Also, find the mean of the random variable.
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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 — Geometric Distribution
Geometric Distribution
Roll a die and wait for the first six. Maybe it comes on the first roll, maybe the tenth. The geometric distribution models exactly this: the number of independent, identical trials needed to get the first success.
The Intuition
Getting the first success on the k-th trial means the first k−1 trials all failed and the k-th succeeded: F, F, …, F, S. Since trials are independent, that exact sequence has probability (1−p)k−1p, where p is the success probability of a single trial.
P(X=k)=(1−p)k−1p,k=1,2,3,…
Here X is the trial on which the first success occurs. Its key summaries are
E[X]=p1,Var(X)=p21−p.
The mean matches intuition: if success has probability 61, you expect about 6 rolls to see the first six.
There are two conventions. Some texts let X count trials until the first success (support 1,2,3,…, as above); others let Y count failures before it (support 0,1,2,…, with P(Y=y)=(1−p)yp and mean p1−p). Check whether the smallest value is 1 or 0.
The Memoryless Property
The geometric distribution is the only discrete distribution that "forgets" the past:
P(X>m+n∣X>m)=P(X>n).
If you have already failed 10 times, the chance of needing 5 more trials is the same as if you were starting fresh — the trials carry no memory, which is why "I'm due for a win" is a fallacy.
When to Use It …
Concept: Binomial-type distribution and its mean (a); repeated independent trials / geometric series (b).
Part (a)
P(red)=P(green)=21, draws independent (with replacement); X= number of red in 2 draws, so X∼B(2,21):
P(X=0)=41,P(X=1)=2⋅21⋅21=21,P(X=2)=41. …
- X∼B(2,21): P(0)=41,P(1)=21,P(2)=41, mean =1.
- P(A)=116, P(B)=115.
Part (a): number of red balls in two draws with replacement
Equal red and green, so each draw gives red with probability 21; with replacement the draws are independent. Let X be the number of reds in 2 draws, X∈{0,1,2}.
P(X=0)=P(GG)=21⋅21=41,
P(X=1)=P(RG)+P(GR)=41+41=21,
P(X=2)=P(RR)=41.
| X | 0 | 1 | 2 |
|---|---|---|---|
| P(X) | 41 | 21 | 41 |
Mean: …
Method: Count-distributions with replacement, and alternating-turn games
Two standard techniques appear here: a binomial distribution for a count under independent (with-replacement) draws, and a geometric-series sum for a "keep going until success" game.
Steps
Step 1 (distribution): identify independent identical trials.
"With replacement" makes draws independent with a fixed success probability p; the count of successes in n draws is X∼B(n,p), with
P(X=r)=(rn)pr(1−p)n−r,E(X)=np.
Step 2 (alternating game): sum a geometric series over a player's winning throws.
If a player throws on turns 1,3,5,… and wins with probability p each throw (failure q), that player's win probability is
p+q2p+q4p+⋯=1−q2p. …
Common Mistakes
Mistake 1 (part a): Forgetting the two arrangements for one red.
Why it's wrong: P(X=1) counts both RG and GR, giving 2⋅21⋅21=21, not 41. Correct approach: use (12) (or list RG, GR).
Mistake 2 (part b): Using 1−qp instead of 1−q2p for the first player. …
Showing the 12 most recent of 27 on this concept.
- 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 MARCH1 markMCQQ.For which value of x, the value of p(x) of binomial distribution with parameters n=4 and p=21 becomes maximum?(a) 0(b) 2(c) 3(d) 4
›Reveal solutionSolution
With n=4, p=21, p(x) is maximum at x=2 (the mean).
For B(n,p) with n=4, p=21, the probabilities are
p(x)=(x4)(21)4,(x4)=1,4,6,4,1 for x=0,1,2,3,4. …
- CBSE 2026Set MARCH1 markQ.The probability of failure in a binomial distribution is 0.6 and the number of trials in it is 5. Find the probability of success.
›Reveal solutionSolution
p=1−q=1−0.6=0.4.
…
- CBSE 2025Set ANNUAL1 markMCQQ.A coin is tossed three times, then the probability to get Head at least two times will be -(a) 81(b) 83(c) 21(d) 85
›Reveal solutionSolution
Use the binomial distribution with n=3, p=21, and add P(X=2)+P(X=3).
…
- CBSE 2025Set MARCH1 markMCQQ.If the parameters of a binomial distribution B(n,p) mean =4 and variance =34, the probability, P(X≥5) is equal to :(a) (31)6(b) (32)6(c) 4(32)6(d) (32)5(31)
›Reveal solutionSolution
Solve np=4, npq=34 for n=6, p=32, q=31, then add P(5) and P(6) to get 4(32)6; option (c).
Find the parameters.
q=meanvariance=44/3=31,p=1−q=32,n=p4=2/34=6.
Compute P(X≥5)=P(5)+P(6) with B(6,32):
P(5)=(56)(32)5(31)=6⋅(32)5⋅31=2(32)5,
P(6)=(66)(32)6=(32)6.
…
- CBSE 2025Set MARCH1 markMCQQ.In a binomial distribution, the probability of success is twice as that of failure, then out of 4 trials, the probability of no success is :(a) 272(b) 8116(c) 811(d) 161
›Reveal solutionSolution
From p=2q and p+q=1 we get q=31; the probability of no success in 4 trials is q4=811, option (c).
Find p and q. Given p=2q and p+q=1:
2q+q=1⟹q=31,p=32.
No success in 4 trials (X=0): …
- CBSE 2025Set MARCH1 markMCQQ.The binomial distribution has mean 6 and variance 712. What will be the type of this distribution?(a) Positively skewed(b) Negatively skewed(c) Symmetric(d) Nothing can be said about the distribution
›Reveal solutionSolution
Since q=meanvariance=72 gives p=75>0.5, the binomial distribution is negatively skewed — option (b).
GSEB Class-12 Statistics, Binomial Distribution:
For a binomial distribution, mean =np and variance =npq. Therefore:
q=meanvariance=612/7=4212=72
p=1−q=1−72=75≈0.714
…
- CBSE 2025Set MARCH1 markQ.Mean of a symmetrical binomial distribution is 9. Find the value of its parameter n.
›Reveal solutionSolution
For a symmetric binomial distribution p=q=21; then np=9⇒n=18.
GSEB Class-12 Statistics, Binomial Distribution:
A binomial distribution is symmetric only when p=q=21.
…
- CBSE 2025Set 465/S/WXYZ/41 markMCQQ.If the mean and standard deviation of a binomial distribution are 12 and 2 respectively, then the value of the parameter p is : (A) 65 (B) 61 (C) 31 (D) 32
›Reveal solutionSolution
From mean np=12 and variance npq=4, we get q=31, so p=1−q=32.
Binomial: mean =np, variance =npq, standard deviation =npq, with q=1−p.
- Mean: np=12.
- Standard deviation =2⇒ variance npq=22=4. …
- CBSE 2024Set D1 markMCQQ.A coin is tossed 10 times. The probability of getting exactly six heads is(a) 10C6(21)6(b) 10C6(21)7(c) 10C6(21)8(d) 10C6(21)10
›Reveal solutionSolution
Binomial: P(X=6)=10C6(21)6(21)4=10C6(21)10.
A coin toss is a Bernoulli trial with p=P(head)=21. For n=10 tosses, the number of heads follows a binomial distribution:
…
- CBSE 2024Set ANNUAL1 markQ.If in a binomial distribution mean is 5 and variance is 4, then write the number of trials.
›Reveal solutionSolution
For a binomial distribution, mean =np and variance =npq. Dividing gives q, then p, then n.
For a binomial distribution B(n,p), mean =np=5 and variance =npq=4.
Dividing variance by mean: q=npnpq=54.
…
- CBSE 2024Set ANNUAL1 markMCQQ.The mean and variance of a binomial distribution are 3 and 3/2, then P(X≥1) is -(a) 64/63(b) 63/64(c) 1/64(d) 1/63
›Reveal solutionSolution
Use mean =np, variance =npq to find n and p, then compute P(X≥1)=1−P(X=0).
Given np=3 and npq=23.
Dividing: q=npnpq=33/2=21, so p=1−q=21.
From np=3: n⋅21=3⇒n=6
…
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