Binomial Distribution – Solved Problems

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Binomial Distribution – Solved Problems with Step-by-Step Solutions

Binomial Distribution – Solved Problems

The binomial distribution is a discrete probability distribution that models the number of successes in a fixed number of independent trials, where each trial has the same probability of success. This topic is fundamental in statistics, probability theory, data science, and competitive exams.

P(X = x) = C(n, x) · px · qn−x
where q = 1 − p
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Problem 1: Basic Binomial Probability

A coin is tossed 5 times. Find the probability of getting exactly 3 heads.

Solution:

Number of trials, n = 5 Probability of head, p = 1/2 Probability of tail, q = 1/2 Number of successes, x = 3

P(X = 3) = C(5,3) × (1/2)3 × (1/2)2
P(X = 3) = 10 × (1/32) = 5/16

Answer: Probability = 5/16

Problem 2: Manufacturing Defects

The probability that a product is defective is 0.1. If 6 products are selected at random, find the probability that exactly 2 are defective.

Solution:

n = 6, x = 2 p = 0.1, q = 0.9

P(X = 2) = C(6,2) × (0.1)2 × (0.9)4
P(X = 2) = 15 × 0.01 × 0.6561 = 0.0984

Answer: Probability ≈ 0.098

Problem 3: Exam Pass Probability

The probability that a student passes an exam is 0.7. If 4 students appear independently, find the probability that at least 3 students pass.

Solution:

“At least 3 pass” means X = 3 or X = 4

P(X ≥ 3) = P(3) + P(4)

For X = 3:

P(3) = C(4,3) × (0.7)3 × (0.3)

For X = 4:

P(4) = C(4,4) × (0.7)4
P(X ≥ 3) = 0.4116 + 0.2401 = 0.6517

Answer: Probability ≈ 0.652

Problem 4: Mean and Variance

For a binomial distribution with n = 10 and p = 0.4, find the mean and variance.

Solution:

Mean = np Variance = npq

q = 1 − 0.4 = 0.6

Mean = 10 × 0.4 = 4 Variance = 10 × 0.4 × 0.6 = 2.4

Answer: Mean = 4, Variance = 2.4

Problem 5: Multiple Choice Questions

A multiple-choice question has 4 options, only one of which is correct. If a student guesses the answer to 5 such questions randomly, find the probability that exactly 2 answers are correct.

Solution:

p = 1/4, q = 3/4, n = 5, x = 2

P(X = 2) = C(5,2) × (1/4)2 × (3/4)3
P(X = 2) = 10 × (1/16) × (27/64) = 135/512

Answer: Probability = 135/512

Problem 6: At Most Probability

A biased coin has probability of heads equal to 0.6. If the coin is tossed 3 times, find the probability of getting at most one head.

Solution:

“At most one head” means X = 0 or X = 1

P(X ≤ 1) = P(0) + P(1)
P(0) = (0.4)3 = 0.064
P(1) = C(3,1) × 0.6 × (0.4)2 = 0.288
P(X ≤ 1) = 0.064 + 0.288 = 0.352

Answer: Probability = 0.352

Problem 7: Real-Life Interpretation

The probability that a machine produces a defective item is 0.05. If the machine produces 8 items, what is the probability that none of them is defective?

Solution:

p = 0.05, q = 0.95, n = 8

P(X = 0) = (0.95)8
P(X = 0) ≈ 0.6634

Answer: Probability ≈ 0.663

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Conclusion: Binomial distribution helps model real-life success–failure experiments. Understanding its formula, assumptions, and applications is essential for probability, statistics, and data analysis.

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