Probability of 5 Heads in Binomial Distribution

In summary, the conversation discusses the probability of getting 5 heads when flipping a coin with 4 fair sides 5 times. It is determined that the probability is (0.25)^5 or 1/(4^5), and the two expressions provided are not equivalent. The concept of equally likely events does not apply in this case, as the coin is not equally weighted.
  • #1
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1,169
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Suppose you have a coin with 4 fair sides, flip it 5 times, and want to know the probability of 5 heads. This is
K(10,5) * (0.25)5 * (1-0.25)5 = K(10,5)*0.255*0.755
Or more generally for any binomially distributed outcome:

1) p(x=r) = pr*(1-p)n-r*K(n,r)

But also we must have that:

2) p(x=r) = K(n,r)/total combinations = K(n,r)/4n
How do you show that 1) and 2) are equivalent?
 
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  • #2
They're not equivalent. Your concept in 2) is not applicable in this case because the events are not equally likely.

Take for example a single coin flip of a fair coin where p=1-p=1/2. The probability of heads is the number of events resulting in heads divided by the total number of possible outcomes or 1/2. All events are equally likely. Now consider a weighted coin where p=3/4=p(heads). The number of ways to get heads is still 1 and the total number of possible outcomes is still 2 but p(heads) does not equal 1/2.
 
  • #3
hmm...

you were to suppose that each side was equally probable :)
 
  • #4
Ooops, I'm really sorry, you have a 4 side coin. My bad.

Anyway, your expressions are incorrect. I'm assuming only one side of the coin is heads. In that case, the probability of 5 heads is (0.25)^5=(1/4)^5.

Alternatively, the number of ways to get 5 heads is 1 and the number of possible outcomes is 4^5, so P(5 heads)=1/(4^5). Same result. Where did the 10 come from?
 
  • #5


To show that 1) and 2) are equivalent, we can start by expanding the first equation using the formula for K(n,r):

1) p(x=r) = pr*(1-p)n-r*K(n,r)
= pr*(1-p)n-r * n!/(r!(n-r)!)
= pr*(1-p)n-r * n*(n-1)*(n-2)*...*(n-r+1)/(r*(r-1)*(r-2)*...*1)
= (n^r * p^r * (1-p)^n-r) / (r!)

Next, we can expand the second equation using the formula for K(n,r) and the fact that there are 4n total combinations:

2) p(x=r) = K(n,r)/total combinations
= n!/(r!(n-r)!) / 4n
= (n*(n-1)*(n-2)*...*(n-r+1))/(r*(r-1)*(r-2)*...*1) / 4n
= (n^r * (n-1)^r * (n-2)^r * ... * (n-r+1)^r)/(r! * (n-r)! * 4n)

We can see that both equations have the terms n^r and r!, but the rest of the terms are different. However, we can simplify the second equation by cancelling out some terms:

= (n^r * (n-1)^r * (n-2)^r * ... * (n-r+1)^r)/(r! * (n-r)! * 4n)
= (n^r * (n-1)^r * (n-2)^r * ... * (n-r+1)^r)/(r! * (n-r)! * 4^r * n^r)
= (1 * (1-1/n)^r * (1-2/n)^r * ... * (1-(r-1)/n)^r)/(r!)

We can then use the binomial theorem to expand the terms in the numerator:

= (1 - r/n + (r*(r-1))/(2!*n^2) - (r*(r-1)*(r-2))/(3!*n^3) + ...) / (r!)
= 1 - r/n + (r*(r-1))/(2!*n
 

1. What is the formula for calculating the probability of obtaining 5 heads in a binomial distribution?

The formula for calculating this probability is: P(x=5) = nCx * p^x * (1-p)^(n-x), where n is the number of trials, x is the number of successes (in this case, 5), and p is the probability of success in each trial.

2. How is the probability of obtaining 5 heads in a binomial distribution different from other probabilities?

The probability of obtaining 5 heads in a binomial distribution is different because it follows a specific distribution pattern where there are only two possible outcomes (success or failure) and each trial is independent from the others. This is in contrast to other probabilities where there may be multiple outcomes and/or the trials are not independent.

3. What is the relationship between the number of trials and the probability of obtaining 5 heads in a binomial distribution?

As the number of trials increases, the probability of obtaining 5 heads in a binomial distribution decreases. This is because the more trials there are, the more opportunities there are for different outcomes to occur, making it less likely for exactly 5 heads to be obtained.

4. Can the probability of obtaining 5 heads in a binomial distribution be greater than 1?

No, the probability of obtaining 5 heads in a binomial distribution cannot be greater than 1. This is because probability is a measure of the likelihood of an event occurring, and a probability of 1 means that the event is certain to occur. In a binomial distribution, the sum of all probabilities for different outcomes must equal 1.

5. How can the probability of obtaining 5 heads in a binomial distribution be used in real-world applications?

The probability of obtaining 5 heads in a binomial distribution can be used in various real-world applications, such as in statistics, genetics, and quality control. For example, it can be used to calculate the likelihood of obtaining a certain number of successful outcomes in a series of independent trials, such as the success rate of a new drug or the quality control of a manufacturing process. It can also be used to make predictions and inform decision making in these fields.

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