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Poisson Distribution Calculator

Rare events at a steady rate — where variance equals the mean.

Work out Poisson Distribution. Rare events at a steady rate — where variance equals the mean. Correct where the obvious implementation is not.

Written and maintained by Mohit PatelLast checked August 4, 2026How we build these

Probability of exactly 2 events

22.4042%

mean 3 · sd 1.732

Exactly k22.40418%
At most k42.31901%
At least k80.08517%
Mean3
Standard deviation1.73205

P(k) = λᵏe⁻ᵛ/k!, for events happening independently at a constant average rate. Calls to a helpdesk, arrivals at a queue, decays in a sample. The distinctive feature is that the variance equals the mean, so the standard deviation is √λ. Real count data that is far more spread out than that is over-dispersed, and Poisson is the wrong model for it — a common finding in practice and worth checking before trusting a fit. It is also the limit of the binomial as trials become many and the per-trial probability small, which is why it works for rare events in large populations.

How the Poisson Distribution Calculator works

Enter an average rate and a number of events for the exact probability and both tails. The variance of a Poisson distribution equals its mean, which is the quickest way to tell whether real count data actually fits it.

Also known as: poisson probability calculator · poisson formula calculator · probability of k events · arrival rate probability calculator

Frequently asked questions

What is the Poisson formula?

P(k) = λᵏe⁻ᵏ/k!, where λ is the average number of events per interval. It applies to events occurring independently at a constant average rate.

When should I use Poisson rather than binomial?

When you have a rate rather than a fixed number of trials — calls per hour, defects per metre, decays per second. Poisson is also the limit of the binomial when trials are many and per-trial probability is small.

How do I know if my data is Poisson?

Check whether the variance matches the mean. Real counts are often far more spread out than that — over-dispersed — in which case Poisson is the wrong model and a negative binomial usually fits better.

Can λ be a decimal?

Yes. It is an average rate, not a count, so 2.7 calls an hour is perfectly valid even though you can never observe 2.7 calls.

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