The Poisson(*l**t*) distribution describes the possible number of events that may occur in an exposure of *t* units, where the average number of events per unit of exposure is *l*. A Poisson(*l**t*) distribution is thus the sum of *t* independent Poisson(*l*) distributions. We might intuitively guess then that if *l**t* is sufficiently large, a Poisson(*l**t*) distribution will start to look like a Normal distribution, because of Central Limit Theorem, as is indeed the case. A Poisson(1) distribution (see graph below) is quite skewed, so we would expect to need to add together some 20 or so before the sum would look approximately Normal.

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