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Binomial Distribution Calculator

Calculate binomial probabilities, expected value, and variance for discrete trials.

Calculate binomial probabilities

How to Use

  1. Enter the number of independent trials, the probability of success on each trial, and the number of successes you want to evaluate.
  2. Click calculate to view the exact probability, cumulative probabilities, and binomial summary statistics.
  3. Review the distribution table to see how likelihood is allocated across different counts of successes.

What is the Binomial Distribution?

The binomial distribution models the number of successes in a fixed number of independent trials when each trial has the same probability of success. Typical examples include coin flips, pass/fail quality control tests, and customer conversions.

  • n = number of trials
  • k = number of observed successes
  • p = probability of success on each trial
  • q = 1 − p = probability of failure

Probability Mass Function

The probability of observing exactly k successes is given by: P(X = k) = C(n, k) × p^k × (1 − p)^(n − k), where C(n, k) is the binomial coefficient representing the number of unique arrangements.

The expected value is n × p and the variance is n × p × (1 − p), providing quick insight into the center and spread of the distribution.

Frequently Asked Questions

When should I use the binomial distribution?
Use the binomial distribution when you have independent trials, a fixed number of attempts, only two outcomes (success or failure), and a constant probability of success on each trial.
Why do probabilities sometimes not sum to exactly 1?
Floating point rounding can introduce very small errors. This calculator clamps results to stay within [0, 1], so minor discrepancies are due to numerical precision.
How can I analyze more than 21 outcome rows?
For large n the table only shows the first 21 rows to keep the interface readable. Export the probabilities or reduce n to view the full distribution.

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