Understanding Binomial Distribution: Sum Always Equals 1?

In summary, the conversation discusses how the summation of binomial probabilities from i=0 to n always results in 1. This is due to the fact that the binomial coefficients are the coefficients of successive terms in the expansion of (x+y)^n, resulting in a final value of 1 for any n.
  • #1
aaaa202
1,169
2
Is quite easy to understand. What I don't understand though is this:
When you sum over all the binomial probabilities from i=0 to n you should get 1, as this corresponds to the total probability of getting any outcome. I just don't understand what it is, that guarantees that you always get one when you sum over:
Ʃ(p)i(1-p)n-i[itex]\cdot[/itex]K(n,i)
Why is this sum always equal to 1?
 
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  • #2
Hi, 4a,
straight out of the binomial theorem,[tex]\sum_{i=0}^n {\binom n i} p^i (1-p)^{n-i} = (p + (1-p))^n = 1^n = 1[/tex]
 
  • #3
The quick way is to note that the binomial coefficients are the coefficients of successive terms in the expansion of (x+y)^n (see binomial theorem). So your summation is the expansion of (p+(1-p))^n which is equal to 1 for any n.
 

FAQ: Understanding Binomial Distribution: Sum Always Equals 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 with two possible outcomes (such as success or failure).

What is the formula for calculating the binomial distribution?

The formula for calculating the binomial distribution is P(x) = nCx * p^x * (1-p)^(n-x), where n is the number of trials, x is the number of successes, and p is the probability of success in each trial.

Why does the sum of all probabilities in a binomial distribution always equal 1?

The sum of all probabilities in a binomial distribution always equals 1 because the distribution represents all possible outcomes of a given event, and one of these outcomes will always occur. Therefore, the sum of all probabilities must equal 1.

How is the binomial distribution related to the normal distribution?

The binomial distribution is closely related to the normal distribution, as it can be approximated by the normal distribution when the number of trials is large enough. This is known as the central limit theorem.

When is the binomial distribution most commonly used?

The binomial distribution is most commonly used in situations where there are a fixed number of independent trials with two possible outcomes, such as in coin flips, genetics, and quality control in manufacturing. It is also used in statistics and probability theory to model real-world events and phenomena.

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