Binomial Probability Calculator
Calculate binomial probabilities P(X=k), P(X≤k), and P(X≥k) for a given number of trials, successes, and probability.
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P(X = k)
P(X ≤ k)
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P(X ≥ k)
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Mean (μ)
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Std Dev (σ)
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What is Binomial Probability Calculator?
The binomial probability calculator computes the probability of exactly k successes in n independent trials, where each trial has a fixed probability p of success. It also calculates cumulative probabilities: P(X ≤ k) and P(X ≥ k). This is used in statistics, quality control, medical trials, and game theory.
How to use
- 1 Enter n — the total number of trials.
- 2 Enter k — the number of successes you want to find the probability for.
- 3 Enter p — the probability of success on each trial (between 0 and 1).
- 4 P(X=k), P(X≤k), and P(X≥k) are calculated instantly.
Formula
Example calculation
A coin is flipped 10 times (p=0.5). P(X=4): C(10,4) × 0.5⁴ × 0.5⁶ = 210 × 0.0625 × 0.015625 ≈ 0.2051 (20.51%). P(X≤4) ≈ 37.7%. P(X≥4) ≈ 82.8%.
Frequently asked questions
What conditions must be met for binomial probability?
There must be a fixed number of trials n, each trial is independent, each trial has exactly two outcomes (success/failure), and p is constant across all trials.
What is the expected value of a binomial distribution?
The expected value (mean) is μ = n × p. The standard deviation is σ = √(n × p × (1−p)).
When should I use normal approximation instead?
When n is large (typically n > 30) and both np and n(1−p) are greater than 5, the normal distribution provides a good approximation to the binomial.
Can p be greater than 1?
No. Probability must be between 0 and 1 inclusive. A p of 0 means the event never occurs; p of 1 means it always occurs.
What is the difference between binomial and Bernoulli distributions?
A Bernoulli distribution is a single trial (n=1). A binomial distribution is the sum of n independent Bernoulli trials.