Binomial Probability Calculator
The binomial distribution gives the probability of getting exactly k successes in n independent trials when each trial succeeds with probability p. Enter n, p and k to see P(X = k), the cumulative probabilities P(X ≤ k) and P(X ≥ k), and the distribution mean and standard deviation.
n must be an integer 1–10 000, p must lie in [0, 1] and k must be an integer between 0 and n.
P(X = k)
24.61%
0.246094
P(X ≤ k)
62.30%
P(X ≥ k)
62.30%
Mean (expected value) n·p
5.000
Std dev √(n·p·(1−p))
1.581
Assumes independent Bernoulli trials with a constant success probability.
Formula
P(X = k) = C(n, k) · pᵏ · (1 − p)ⁿ⁻ᵏ Mean μ = n · p; Variance σ² = n · p · (1 − p)
- · C(n, k) = n! ÷ (k! · (n − k)!) is the binomial coefficient.
- · Assumes n independent trials, each succeeding with the same probability p (Bernoulli trials).
- · The tool uses the recurrence P(X = i+1) = P(X = i) · (n − i)/(i + 1) · p/(1 − p) to enumerate each PMF, which stays numerically stable up to n = 10,000.
- · For large n with p away from 0 and 1, X is well approximated by N(np, np(1 − p)); the usual rule of thumb is n · p ≥ 10 and n · (1 − p) ≥ 10.
- · When p is small, n is large and n · p ≈ λ, the Poisson approximation P(X = k) ≈ e⁻ᵗ · λᵏ / k! works well.
- · p = 0 puts all mass at k = 0; p = 1 puts all mass at k = n.
Frequently asked
When should I use the binomial vs Poisson or normal distribution?
Use the binomial whenever the number of trials n is fixed and each trial has the same probability p. If n is large and p is small with n · p ≈ λ (rare-event regime), the Poisson approximation works well. When both n · p ≥ 10 and n · (1 − p) ≥ 10, the normal approximation N(np, np(1 − p)) is accurate; near p = 0 or p = 1 it breaks down and you should stick with the exact binomial.
Why don't P(X ≤ k) and P(X ≥ k) sum to 1?
Both tail probabilities include the point k: P(X ≤ k) = P(X < k) + P(X = k) and P(X ≥ k) = P(X > k) + P(X = k). So P(X ≤ k) + P(X ≥ k) = 1 + P(X = k), not 1. For a disjoint split summing to 1, use P(X ≤ k) + P(X > k) or P(X < k) + P(X ≥ k). The widget also shows the strict-inequality variants (P(X < k) and P(X > k)) so you can cross-check.
How large can n be, and how accurate are the results?
The tool supports n up to 10,000. Internally it walks the recurrence P(i+1) = P(i) · (n − i)/(i + 1) · p/(1 − p) starting from P(0) = (1 − p)ⁿ; rounding error sits around 1e-10, far below what you need for normal hypothesis testing (n in the tens to hundreds). For n above 10,000, use the normal or Poisson approximation instead.
Can p be exactly 0 or 1?
Yes. p = 0 means every trial fails, so all the mass sits at k = 0: P(X = 0) = 1 and every other k is 0. p = 1 mirrors that with all mass at k = n. The calculator returns these limits directly rather than dividing by zero.
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