Probability of exactly x heads in n tosses

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I will make sure to add this information to the library. In summary, the probability of getting only five heads in a row when tossing a coin 300 times is approximately 2.64*10^-680. This is calculated by multiplying the probability of exactly 5 heads in 300 tosses (8.8*10^-66) with the probability of the outcomes being ordered in a specific way (3*10^-615). Binomial distribution is used to calculate the probability of x successes in n trials, while Poisson distribution is used when n is large and x is small. Both distributions have their own formulas and can be found in the library for more information.
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moonman239
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Problem: I toss a coin 300 times. What is the probability that I get only five heads in a row?

Solution: The probability of exactly 5 heads in 300 tosses is about 8.8*10^-66, according to the Poisson approximation to the binomial distribution (see below). But, those 5 heads can occur anywhere within the sample set. The probability of the outcomes being ordered so that 1 head is in a given spot in the set = 1 / n! where n is the number of tosses, ! (for those who don't know) is the factorial function (n-1 * n-2 * n-3...n-(n-1)). That number is approximately 3*10^-615.

To get the probability, then, of exactly five heads in a row, simply multiply the two numbers together. That gives me 2.64*10^-680.

Q&A
What is the binomial distribution?

The binomial distribution describes the probability of exactly x successes in a series of n trials. The formula for this distribution is: n!/(x! * (n - x)!).

However, when n is too large and x is too small, this formula is not very computationally efficient. Also, your calculator may throw an error if n is greater than 115, due to the fact that the factorial function can't handle numbers that large. In such cases, the Poisson distribution is more useful.

What is the Poisson distribution?

The Poisson distribution is the probability that, for example, a call center who receives a call every 3 minutes on the average will receive a call 2 minutes after another one.

The formula for this distribution is:

(E(n)^x * e^-E(n)) / x!

where E(n) is the long-run average ("expected") number of successes, and e is Euler's number (2.71828...).

More info
Binomial distribution: http://en.wikipedia.org/wiki/binomial_distribution"
Poisson distribution:http://en.wikipedia.org/wiki/Poisson_distribution"
 
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You might want to add this to the library. See the menu bar at the top and click the green button that says "library". That is made specifically for these kinds of posts...
 
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micromass said:
You might want to add this to the library. See the menu bar at the top and click the green button that says "library". That is made specifically for these kinds of posts...

Thank you for pointing that out.
 

1. What is the formula for calculating the probability of exactly x heads in n tosses?

The formula for calculating the probability of exactly x heads in n tosses is (nCx)(p^x)(q^(n-x)), where n is the total number of tosses, x is the specific number of heads, p is the probability of getting a head, and q is the probability of getting a tail.

2. How do you determine the value of p and q in the probability formula?

The value of p and q can be determined by dividing the number of desired outcomes (heads or tails) by the total number of possible outcomes. For example, if you are flipping a fair coin, p and q would both be 0.5.

3. Can the probability of exactly x heads in n tosses be greater than 1?

No, the probability of exactly x heads in n tosses cannot be greater than 1. It is a measure of likelihood and therefore cannot be larger than 1.

4. How does increasing the number of tosses affect the probability of getting exactly x heads?

The more tosses you have, the closer the probability of getting exactly x heads will be to the expected value. For example, if you toss a coin 10 times, the probability of getting exactly 5 heads is 0.246, but if you toss the coin 100 times, the probability of getting exactly 50 heads is 0.079.

5. Can this formula be used for any type of random event?

Yes, this formula can be used for any type of random event that has a binary outcome (e.g. heads or tails, success or failure). It is a fundamental concept in probability and can be applied to a wide range of situations.

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