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Poisson distribution From Wikipedia, the free encyclopedia
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In probability theory and statistics, the Poisson distribution (pronounced [pwasɔ̃]) is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event.[1] (The Poisson distribution can also be used for the number of events in other specified intervals such as distance, area or volume.)
Suppose someone typically gets 4 pieces of mail per day. That becomes the expectation, but there will be a certain spread: sometimes a little more, sometimes a little less, once in a while nothing at all.[2] Given only the average rate, for a certain period of observation (pieces of mail per day, phonecalls per hour, etc.), and assuming that the process, or mix of processes, that produce the event flow are essentially random, the Poisson distribution specifies how likely it is that the count will be 3, or 5, or 11, or any other number, during one period of observation. That is, it predicts the degree of spread around a known average rate of occurrence.[2]
The distribution's practical usefulness has been explained by the Poisson law of small numbers.[3]
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Dave Wendt says:
It would appear that inadvertently Mr. Masters, or whoever provided him with his numbers, has arrived at a ratio that is quite correct, the only problem being the ratio is applied to the wrong query. If you ask “what are the odds of a story about a human caused plague of horrendous heatwaves, which appears in any Lamestream Media source, NOT being complete BS?” the ratio of 1 in 1.6 million appears, to my eye at least, to be just about spot on.