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Poisson Distribution Calculator

Calculate Poisson probability P(X=k), cumulative event odds, and arrival rates per time or spatial interval.

Exact P(X = 4)
19.54%
At Least P(X ≥ 4)
56.65%
At Most P(X ≤ 4)
62.88%
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What Is a Poisson Distribution Calculator?

The Poisson Distribution models the number of times an event occurs within a fixed interval of time or space. It applies when events happen with a known constant mean rate λ and independently of the time since the last event (e.g. call center calls per hour, website traffic bursts, emergency room admissions).

How to Use This Calculator

  1. Enter the Average Rate of occurrence (λ) per interval (e.g. 4 calls per hour).
  2. Enter the target number of observed events (k).
  3. Optionally adjust the time scale multiplier (e.g. 2 for a 2-hour window).
  4. Review exact P(X = k), cumulative P(X ≤ k), and P(X ≥ k) probabilities.

Poisson PMF and Parameters

P(X = k) = \frac{\lambda^k e^{-\lambda}}{k!}, \quad \mu = \lambda, \quad \sigma^2 = \lambda

Where λ is the average number of events per interval, k is the observed number of events, and e is Euler’s constant (≈ 2.71828). In a Poisson distribution, the mean and variance are uniquely equal to λ.

Worked Example

Scenario: A coffee shop receives an average of 3 customers per minute (λ = 3). What is the probability of exactly 4 customers arriving in the next minute?

Parameters: λ = 3, k = 4.

Calculate numerator: 3^4 × e^(-3) = 81 × 0.049787 = 4.03275.

Calculate denominator: 4! = 24.

Divide: 4.03275 / 24 ≈ 0.1680 (16.80%).

Result: 16.80% probability of exactly 4 customers.

Tips & Key Notes

  • Unlike the binomial distribution which has a fixed maximum number of trials n, the Poisson distribution has no upper limit on k.
  • A unique mathematical hallmark of the Poisson distribution is that its variance is always exactly equal to its mean (σ² = μ = λ).

Frequently Asked Questions

What conditions are required for a Poisson distribution?

Events must occur one at a time, events must be independent of one another, and the average rate λ must remain constant across the measured period.

How do you scale a Poisson rate for different time intervals?

Multiply the base rate by the time factor. If website errors occur at λ = 2 per hour, the rate for an 8-hour shift is λ = 2 × 8 = 16 errors.

How is Poisson related to the exponential distribution?

If the count of events in an interval follows a Poisson distribution with rate λ, the elapsed time between successive events follows an exponential distribution with rate parameter λ.

Can Poisson approximate the Binomial distribution?

Yes. When n is large (n ≥ 100) and p is small (p ≤ 0.01), a binomial distribution is well approximated by a Poisson distribution with λ = n × p.

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