Q.Find the probability distribution of the random variable X, which denotes the number of doublets in four throws of a pair of dice. Hence, find the mean of the number of doublets (X).
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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: …
Concept: Binomial distribution – each throw is an independent Bernoulli trial with success = "doublet."
A doublet occurs when both dice show the same number: (1,1), (2,2), …, (6,6). So P(doublet)=366=61 and P(no doublet)=65.
In four throws, X∼Binomial(n=4,p=61). The probability mass function is
P(X=r)=(r4)(61)r(65)4−r,r=0,1,2,3,4.
Computing each:
| X | P(X) |
|---|---|
| 0 | (04)(65)4=1296625 |
| 1 | (14)(61)(65)3=1296500 |
A doublet occurs when both dice show the same number. We find the probability of k doublets in four throws using the binomial distribution, then compute the mean as 32.
Understanding doublets and the binomial structure
A doublet happens when a pair of dice lands with matching faces: (1,1),(2,2),…,(6,6). In a single throw of two dice, there are 36 equally likely outcomes, and exactly 6 are doublets. So the probability of a doublet in one throw is p=366=61, and the probability of not getting a doublet is q=1−61=65.
When we throw the pair four times independently, the number of doublets X follows a binomial distribution with parameters n=4 and p=61. The random variable X can take values 0,1,2,3,4, and we compute each probability using the binomial formula:
P(X=k)=(kn)pkqn−k=(k4)(61)k(65)4−k
Building the probability distribution
Let's calculate P(X=k) for each possible value:
- For X=0 (no doublets):
P(X=0)=(04)(61)0(65)4=1⋅1⋅1296625=1296625
- For X=1 (exactly one doublet):
P(X=1)=(14)(61)1(65)3=4⋅61⋅216125=1296500=324125
- For X=2 (exactly two doublets):
P(X=2)=(24)(61)2(65)2=6⋅361⋅3625=1296150=21625
- For X=3 (exactly three doublets):
P(X=3)=(34)(61)3(65)1=4⋅2161⋅65=129620=3245
- For X=4 (all four doublets):
P(X=4)=(44)(61)4(65)0=1⋅12961⋅1=12961
The complete probability distribution is:
| X | 0 | 1 | 2 | 3 | 4 |
|-----|-----|-----|-----|-----|-----| …
Method: Distribution and mean of a count of compound-event successes
When each trial is itself a compound experiment (like throwing a pair of dice) and you count how often a described outcome occurs, first find the single-trial success probability, then treat the repeats as binomial.
Steps
Step 1: Find the per-trial success probability.
Work out the probability of the described outcome in one trial from its sample space (e.g. a doublet is 6 of 36 equally likely outcomes, so p=61).
Step 2: Model the repeats as binomial.
For n independent throws, the count X∼B(n,p) with …
Common Mistakes
Mistake 1: Taking P(doublet)=361.
Why it's wrong: there are 6 doublets (1,1),…,(6,6) out of 36 outcomes, so p=366=61, not 361. Correct approach: count all six matching pairs.
Mistake 2: Re-deriving the mean the long way (and mis-adding). …
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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