Binomial Distribution Calculator

Evaluate probabilities and cumulative tails for Binomial(n, p). Enter the number of trials, probability of success, and observed successes to explore the distribution.

P(X = 5)

22.70%

Probability of exactly k successes

P(X ≤ 5)

66.52%

Cumulative probability up to k

P(X ≥ 5)

56.18%

Upper tail probability

Mean: 4.80 Variance: 2.88 Std. Dev: 1.70

kP(X = k)P(X ≤ k)
00.00220.0022
10.01740.0196
20.06390.0834
30.14190.2253
40.21280.4382
50.22700.6652
60.17660.8418
70.10090.9427
80.04200.9847
90.01250.9972
100.00250.9997
110.00031.0000
120.00001.0000

How to Use This Calculator

  1. Specify the number of trials (n) for your binomial experiment.
  2. Enter the probability of success on each trial (p).
  3. Provide the number of successes (k) you want to evaluate.
  4. Review the probability tables, tail probabilities, and summary statistics.

Formula Reference

P(X = k) = C(n, k) · pk · (1 − p)n − k

Mean = n · p

Variance = n · p · (1 − p)

Std. Deviation = √(n · p · (1 − p))

This distribution models the number of successes in independent Bernoulli trials. When np(1 − p) is large, the normal approximation with continuity correction provides a quick estimate.

Frequently Asked Questions

What if the probability changes between trials?

The binomial model requires a constant probability. If p varies, consider the Poisson binomial distribution which aggregates different probabilities.

When is the normal approximation valid?

Rules of thumb: n · p ≥ 10 and n · (1 − p) ≥ 10. Apply a continuity correction by evaluating ranges like P(k − 0.5 ≤ X ≤ k + 0.5).

Can I compute cumulative probabilities without summing k values?

Statistical libraries implement efficient algorithms. This calculator precomputes the PMF table and cumulative totals for you.

Is fractional n allowed?

No. The number of trials must be an integer, though expected counts (n · p) can be fractional.