Poisson vs Binomial approaches yield different results

The Poisson distribution is a limiting case of the Binomial, as the number of trials goes to infinity while the probability of success goes to 0, but that case is not applicable to the OP problem.
  • #1
DocZaius
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11

Homework Statement



I made this question for myself to try to see if I could use two approaches (Poisson Distribution and Binomial Distribution) to solve a problem:

Someone's average is to make 1 out of every 3 basketball shots.
What are the chances she makes exactly 2 shots in a trial of 3 attempts?

Homework Equations



Poisson Distribution:
(λ^k)(e^-λ)/(k!)

Binomial Distribution:
(n choose k)(P^k)((1-P)^(n-k))

The Attempt at a Solution



Poisson approach:
λ is the average of successes in a given series length = 1
k is the queried amount of successes in the same series length = 2
Answer = (1^2)(e^-1)/(2!) = .184

Binomial approach:
n is the number of attempts = 3
k is the number of queried successes = 2
P is the probability of success = (1/3)
Answer = (3 choose 2) (1/3)^2 (2/3)^1 = .222

I have a feeling using Poisson here is wrong but I am not sure why. I have seen this type of problem used as an example for use of both Poisson and Binomial distributions.

Thanks.
 
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  • #2
DocZaius said:

Homework Statement



I made this question for myself to try to see if I could use two approaches (Poisson Distribution and Binomial Distribution) to solve a problem:

Someone's average is to make 1 out of every 3 basketball shots.
What are the chances she makes exactly 2 shots in a trial of 3 attempts?

Homework Equations



Poisson Distribution:
(λ^k)(e^-λ)/(k!)

Binomial Distribution:
(n choose k)(P^k)((1-P)^(n-k))

The Attempt at a Solution



Poisson approach:
λ is the average of successes in a given series length = 1
k is the queried amount of successes in the same series length = 2
Answer = (1^2)(e^-1)/(2!) = .184

Binomial approach:
n is the number of attempts = 3
k is the number of queried successes = 2
P is the probability of success = (1/3)
Answer = (3 choose 3) (1/3)^2 (2/3)^1 = .222

I have a feeling using Poisson here is wrong but I am not sure why. I have seen this type of problem used as an example for use of both Poisson and Binomial distributions.


Thanks.

The Poisson is wrong because it is not at all designed for this type of problem. Basically, the Poisson distribution is for counting events that can occur at any time, but when we want to know the probabilities of the number occurring in a particular interval. Your problem is different: events are "discrete"---think of them as occurring at times 1,2 and 3, for example. Under certain circumstances the Poisson can be used as an approximation to the Binomial which may be much easier to work with, but the circumstances where that holds are not present in your problem. I suggest you do a Google search on "Poisson distribution" for more information.
 
  • #3
In a Poisson process, there's no limit to the number of successes you could get. In the OP problem, there cannot be more than three successes. So modelling it as Poisson means you will likely underestimate the probabilities of 0, 1, 2 or 3 successes, to balance the overestimation of more than 3.
 

What is the difference between Poisson and Binomial approaches?

The Poisson approach is used to model the number of occurrences of an event in a fixed time or space, while the Binomial approach is used to model the number of successes in a fixed number of trials.

Why do Poisson and Binomial approaches yield different results?

The Poisson distribution assumes that the events occur independently and at a constant rate, while the Binomial distribution assumes that each trial is independent and has a fixed probability of success. Therefore, the underlying assumptions of the two approaches are different, leading to different results.

In what situations would you use a Poisson approach?

A Poisson approach is typically used for rare events that occur independently, such as the number of car accidents in a day or the number of customers entering a store in an hour.

In what situations would you use a Binomial approach?

A Binomial approach is typically used for events that have a fixed number of trials and a known probability of success, such as the number of heads in 10 coin flips or the number of students who pass a test out of a class of 50.

Can a Poisson distribution be used as an approximation for a Binomial distribution?

Yes, in certain situations where the number of trials is large and the probability of success is small, a Poisson distribution can be used as an approximation for a Binomial distribution. This is known as the Poisson approximation to the Binomial distribution.

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