Finding success probability given a binomial probability?

Thread starter moonman239
Start date Jan 20, 2011
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Binomial Probability

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Jan 20, 2011

moonman239

Is there a way to calculate, say, the probability of a dice landing on an 11, given that the binomial probability of getting exactly six elevens in 100 tosses is 24.6%?

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Jan 20, 2011

tiny-tim

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hi moonman239!

yes … call the probability of one 11 p …

then what is the formula for the probability of six 11s in 100 ?

1. What is a binomial probability?

A binomial probability refers to the likelihood of a specific outcome occurring in a series of independent trials. It is based on the principles of probability and is often used to calculate the chances of success or failure in an experiment or study.

2. How is success probability determined in a binomial experiment?

The success probability in a binomial experiment is typically represented by the letter "p". It is calculated by dividing the number of successful outcomes by the total number of outcomes in the experiment. For example, if you toss a coin 10 times and get 7 heads, the success probability would be 7/10 or 0.7.

3. What is the formula for finding success probability given a binomial probability?

The formula for calculating success probability in a binomial experiment is: P(x) = nCx * p^x * (1-p)^(n-x), where n is the total number of trials, x is the number of successful outcomes, and p is the success probability.

4. Can the success probability change in a binomial experiment?

Yes, the success probability can change in a binomial experiment depending on the conditions of the experiment. For example, if the experiment involves flipping a coin and the coin is weighted, the success probability may change. Additionally, if the conditions of the experiment change, such as the number of trials or the definition of success, the success probability may also change.

5. How can I use binomial probability to predict success in future experiments?

Binomial probability can be used to make predictions about the likelihood of success in future experiments. By calculating the success probability and understanding the factors that may influence it, you can make informed predictions about the outcome of future trials. However, it is important to note that probability is not a guarantee and there is always a chance that the actual results may differ from the predicted outcome.