Binomial Distribution Calculator

Binomial Distribution Calculator

Calculate probabilities for binomial distributions with n trials and probability p

Binomial Distribution Calculator: Complete Statistical Guide

The binomial distribution is a discrete probability distribution that models the number of successes in a fixed number of independent trials.Each trial has the same probability of success, making it ideal for modeling scenarios like coin flips, quality control tests, survey responses, and clinical trials.

Our professional binomial distribution calculator provides probability analysis including exact probabilities, cumulative distributions, range calculations, and complete statistical measures like mean, variance, and distribution characteristics.

Quick Answer

For binomial distribution: You need number of trials (n), probability of success (p), and number of successes (k). The probability of exactly k successes is P(X = k) = C(n,k) × p^k × (1-p)^(n-k). For example, the probability of getting exactly 3 heads in 5 coin flips is approximately 31.25%.

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Mathematical Foundation

P(X = k) = C(n,k) × p^k × (1-p)^(n-k)

Binomial probability formula where C(n,k) is combinations, p is success probability, and (1-p) is failure probability

Key Distribution Parameters:

Mean (Expected Value)

μ = n × p. The expected number of successes in n trials with probability p.

Variance

σ² = n × p × (1-p). Measures the spread of the distribution around the mean.

Standard Deviation

σ = √(n × p × (1-p)). Square root of variance, measuring typical deviation from the mean.

Common Applications

Quality Control & Manufacturing

Defect Analysis

Model number of defective items in production batches

Pass/Fail Testing

Analyze success rates in component testing

Medical & Clinical Research

Drug Efficacy

Model patient response rates to treatments

Clinical Trials

Analyze success rates in medical interventions

Example Problems with Solutions

Example 1: Coin Flipping

What's the probability of getting exactly 3 heads in 5 fair coin flips?

n = 5 trials, p = 0.5, k = 3 successes
C(5,3) = 5!/(3!×2!) = 10
P(X = 3) = 10 × (0.5)³ × (0.5)² = 10 × 0.125 × 0.25
P(X = 3) = 0.3125 = 31.25%

Answer: 31.25% chance of exactly 3 heads

Example 2: Quality Control

In a batch of 100 items with 5% defect rate, what's the probability of finding 3 or fewer defects?

n = 100, p = 0.05, k ≤ 3 (cumulative)
P(X ≤ 3) = P(X=0) + P(X=1) + P(X=2) + P(X=3)
Using binomial formula for each term
P(X ≤ 3) ≈ 0.2578 = 25.78%

Answer: 25.78% chance of 3 or fewer defects

Frequently Asked Questions

When should I use the binomial distribution?

Use binomial distribution when you have: (1) Fixed number of trials, (2) Each trial has only two outcomes (success/failure), (3) Probability of success is constant, and (4) Trials are independent. Common examples include coin flips, quality control, and survey responses.

What's the difference between exact and cumulative probability?

Exact probability P(X = k) is the chance of getting exactly k successes.Cumulative probability P(X ≤ k) is the chance of getting k or fewer successes. Use exact for specific outcomes, cumulative for "at most" or "at least" questions.

How do I interpret the mean and standard deviation?

The mean (μ = np) tells you the expected number of successes. The standard deviationindicates variability - smaller values mean results cluster near the mean, larger values indicate more spread. About 68% of outcomes fall within one standard deviation of the mean.

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