Prob. for average value or less in binomial distribution?

In summary, there is no closed form expression for the probability of a binomial distribution yielding the average np or less. However, for large n, the binomial distribution can be approximated by the normal distribution when p is fixed. In the simplified case of p=1/n, the sum goes to 2/e (about 73%) as n->infty. For more general cases, the Poisson distribution is a good approximation. Unfortunately, using the normal distribution yields 50%, which is not accurate when considering the discrete nature of the problem. The Poisson distribution can be used when considering two variables, n and p, with the mean being np. This problem arose when trying to determine the likelihood of a "statistical
  • #1
Gerenuk
1,034
5
Hello!
Is there a closed form expression or a good estimate for the probability that a binomial distribution yield the average np or less. Basically I'm asking for a good way to evaluate
[tex]
P=\sum_{k=0}^{np} \begin{pmatrix} n\\ k
\end{pmatrix} p^k(1-p)^{n-k}
[/tex]

I just figured that for the simplified case [itex]p=\frac{1}{n}[/itex] this probability converges to 63% for large n. What about more general cases?
 
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  • #2
There is no closed form expression for Prob. (other than the exact formula). However for large n, the binomial can be approximated by the normal distribution when p is fixed. In the case you are describing (np fixed), the Poisson distribution is a good approximation.
 
  • #3
For the simplified case of p=1/n, your sum goes to 2/e (about 73%) as n->infty.
 
  • #4
mathman said:
There is no closed form expression for Prob. (other than the exact formula). However for large n, the binomial can be approximated by the normal distribution when p is fixed. In the case you are describing (np fixed), the Poisson distribution is a good approximation.
Unfortunately using the normal distribution yields 50%, which is not true when the discrete character isn't lost. For Poisson I'm not sure where to put in my 2 variables n and p :(

Btw, this problem I thought of when trying to think of how likely a "statistical statement" would be. With the odds 1:N for example you can be 63% ([itex]1-e^{-1}[/itex]) sure that at least 1 of a N people is "positive".
 
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  • #5
For Poisson I'm not sure where to put in my 2 variables n and p
Mean(Poisson) = np.
 

1. What is the binomial distribution?

The binomial distribution is a probability distribution that describes the likelihood of obtaining a certain number of successes in a fixed number of independent trials, where the probability of success remains constant for each trial.

2. What is the average value in a binomial distribution?

The average value in a binomial distribution is also known as the mean or expected value. It is calculated by multiplying the number of trials by the probability of success for each trial. In mathematical notation, it can be represented as "n * p".

3. How do you calculate the probability for an average value or less in a binomial distribution?

To calculate the probability for an average value or less in a binomial distribution, you can use the cumulative distribution function (CDF). This function takes in the number of trials, probability of success, and the desired average value, and gives the probability of obtaining that value or less.

4. Can the probability for an average value or less in a binomial distribution be greater than 1?

No, the probability for an average value or less in a binomial distribution cannot be greater than 1. This is because probabilities are always between 0 and 1, where 0 represents impossibility and 1 represents certainty.

5. How does sample size affect the probability for an average value or less in a binomial distribution?

The sample size, or number of trials, can affect the probability for an average value or less in a binomial distribution. Generally, as the sample size increases, the probability for a certain average value or less also increases. This is because a larger sample size allows for a more accurate representation of the overall population.

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