Q.In a batch inspection, the probability that any single item is defective is 0.1. If 6 items are picked independently, find the probability that at most 1 item is defective.
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:
- Mean (expected number of successes): μ=np
- Variance: σ2=np(1−p)
The mean makes intuitive sense: if you flip a coin 100 times with p=0.5, you expect about 50 heads. The variance tells you how spread out the distribution is — it's largest when p=0.5 (maximum uncertainty) and smallest when p is near 0 or 1 (outcome is almost certain).
The binomial distribution is the foundation for many statistical tests and is closely related to the normal distribution — for large n, a binomial distribution with np and np(1−p) both at least 5 can be approximated by a normal distribution with the same mean and variance.
When Not to Use It
The binomial distribution fails if any condition is violated. Common exam traps:
- Without replacement from a finite population — use hypergeometric.
- Trials continue until a success — use geometric distribution.
- Counting events in a fixed interval — use Poisson distribution.
The binomial distribution is your tool when you have a fixed number of independent, identical trials, each with two outcomes. Master this, and you've unlocked a core piece of probability.
Binomial distribution is a core topic of the NCERT Class 12 Mathematics chapter on Probability, and searches like "binomial distribution formula and examples" or "binomial distribution class 12 important questions" reflect how frequently it appears in CBSE board papers and JEE Main statistics questions. The mean-variance relationship and the contrast with Poisson and hypergeometric distributions covered above are exactly the distinctions competitive exams like to test.
Picking 6 items independently, each with a constant 0.1 chance of being defective, is a binomial situation with n = 6, p = 0.1; "at most 1" means 0 or 1 defective.
P(X≤1) = P(X=0) + P(X=1), computed from n=6, p=0.1, q=0.9.
P(at most 1 defective) ≈ 0.8857
Here n=6, p=0.1 (defective), q=0.9 (non-defective). "At most 1 defective" means X=0 or X=1:
P(X=0)=(06)(0.1)0(0.9)6=(0.9)6=0.531441
P(X=1)=(16)(0.1)1(0.9)5=6×0.1×0.59049=0.354294
P(X≤1)=0.531441+0.354294=0.885735
P(at most 1 defective) ≈ 0.8857
Mixing up which outcome is labelled "success" is a common trap — here it is cleaner to treat "defective" as success with p = 0.1, but some students instead set p = 0.9 for "non-defective" and then answer a differently-worded question. Always re-read what x is meant to count before plugging into the formula.
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
For a sample of 5 bulbs (treated as 5 independent Bernoulli trials, as is standard for this kind of MCQ), the probability that none of the 5 is defective — i.e. all 5 are good — is
P(none defective)=(109)5
✓Final answerThe correct option is (c) (109)5.
- 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.
The largest coefficient is (24)=6, so p(x) is maximum at x=2, which is also the mean np=2.
✓Final answer(b) 2.
- 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.
For a binomial distribution the success probability p and failure probability q satisfy p+q=1. Given q=0.6 (the number of trials n=5 is not needed here),
p=1−q=1−0.6=0.4.
✓Final answerp=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).
P(at least 2 heads)=P(2)+P(3)=(23)(21)3+(33)(21)3=83+81=84=21
✓Final answerThe correct option is (c) 21.
- 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.
Add (factor (32)5):
P(X≥5)=(32)5(2+32)=(32)5⋅38=4(32)6.
Numerically =729256.
✓Final answerOption (c) 4(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):
P(X=0)=(04)p0q4=q4=(31)4=811.
✓Final answerOption (c) 811.
- 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
Skewness rule for a binomial distribution:
- p<0.5 : positively skewed
- p=0.5 : symmetric
- p>0.5 : negatively skewed
Here p=75>0.5, so the distribution is negatively skewed.
✓Final answer(b) Negatively skewed.
- 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.
The mean of a binomial distribution is np. Given mean =9:
np=9
n×21=9
n=18
✓Final answern=18.
- 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.
- Divide: npnpq=q=124=31.
- Then p=1−q=1−31=32.
✓Final answer(D) 32
- 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:
P(X=6)=10C6p6(1−p)10−6=10C6(21)6(21)4=10C6(21)10.
✓Final answer(d) 10C6(21)10.
- 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.
So p=1−q=1−54=51.
From the mean, np=5⇒n=p5=1/55=25.
✓Final answerThe number of trials is n=25.
- 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
P(X=0)=qn=(21)6=641
P(X≥1)=1−P(X=0)=1−641=6463
✓Final answerP(X≥1)=6463 — option (b).
🎓Unlock everything free for 14 days
- ✓Full step-by-step solutions
- ✓Concept-first explanations
- ✓Methods, shortcuts & mistakes
- ✓PYQ mapping + timed mock tests
Full access for 14 days. No credit card required.