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Poisson Distribution Calculator

The Poisson distribution gives the probability of seeing a certain number of events in a fixed interval when you know the average rate. Enter the average λ and a count k to get the probability of exactly k events and the cumulative probabilities either side.

Quick examples

The mean count per interval — for example 3 calls per hour.

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P(X = k) exactly k0.224042
P(X ≤ k) at most k
0.42319
P(X ≥ k) at least k
0.800852
P(X < k) fewer than k
0.199148
P(X > k) more than k
0.57681
Standard deviation (√λ)
1.7321
Distribution around the mean
kP(X = k)P(X ≤ k)

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      Formula

      P(X = k) = λᵏ × e^(−λ) ÷ k!
      P(X ≤ k) = P(X = 0) + P(X = 1) + … + P(X = k)
      Mean = λ; variance = λ; standard deviation = √λ

      How to use it

      1. Enter the average number of events per interval, λ.
      2. Enter the number of events you are asking about, k.
      3. Pick the line that matches your question: exactly k, at most k, at least k, fewer than k or more than k.

      Worked examples

      An average of 3 events, and exactly 2 happen

      P(X = k) exactly k
      0.224042
      P(X ≤ k) at most k
      0.42319
      P(X ≥ k) at least k
      0.800852
      Standard deviation (√λ)
      1.7321

      A call center averaging 4.5 calls an hour gets exactly 6

      P(X = k) exactly k
      0.12812
      P(X ≤ k) at most k
      0.831051
      P(X > k) more than k
      0.168949

      No defects when the average is 0.8 per unit

      P(X = k) exactly k
      0.449329
      P(X ≥ k) at least k
      1

      When to use it

      Poisson fits counts of events that happen independently at a steady average rate: calls arriving per hour, typos per page, defects per roll of fabric, goals per match. There is no fixed number of trials — that is what separates it from the binomial distribution.

      It is also a good approximation to the binomial when there are many trials and success is rare; use λ = n × p.

      Match λ to the interval

      λ must be the average for the same interval you are asking about. If a help desk averages 3 calls an hour, use λ = 1.5 for a half-hour window and λ = 24 for an eight-hour shift.

      Questions people ask

      If the average is 3 events, what is the probability of exactly 2?

      22.40% (0.224042), from 3² × e⁻³ ÷ 2!.

      With an average of 3, what is the probability of 2 or fewer events?

      42.32% (0.423190).

      What is the probability of zero events when the average is 0.8?

      44.93%, which is simply e^(−0.8).

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