Binomial Distribution: Finding the number of trials

In summary: However, this is beyond the scope of this assignment. In summary, you would need to find the number of trials needed to be 90% sure of at least three or more success, given that probability of one success is 0.2.
  • #1
Dommm
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0

Homework Statement


Question: Find the number of trials needed to be 90% sure of at least three or more success, given that probability of one success is 0.2


Homework Equations



N/A

The Attempt at a Solution



My initial attempt at the problem was finding the probability of at least one success, then multiplying it by three, however that isn't the correct answer. I've found the answer via trial and error using binomial calculators online, but I still cannot seem to work it out by math.

Is it even possible? or is it far too complicated to find the number of trials for something for least two or more successes?
 
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  • #2
Dommm said:
My initial attempt at the problem was finding the probability of at least one success, then multiplying it by three, however that isn't the correct answer.
No, you don't calculate it that way. In fact, think about it for a moment - it's easier to get at least one success than to get at least three successes, so the probability of at least three successes should be LOWER, not higher.

To find the probability, observe that the complement of "at least three successes" is "no more than two successes".

So you can compute
$$P(\text{at least three successes}) = 1 - P(\text{zero, one, or two successes})$$
 
  • #3
jbunniii said:
No, you don't calculate it that way. In fact, think about it for a moment - it's easier to get at least one success than to get at least three successes, so the probability of at least three successes should be LOWER, not higher.

To find the probability, observe that the complement of "at least three successes" is "no more than two successes".

So you can compute
$$P(\text{at least three successes}) = 1 - P(\text{zero, one, or two successes})$$

Furthermore, you can calculate a formula for ##P(\text{zero, one, or two successes})## in terms of ##n##, using the formula for binomial probabilities.
 

Related to Binomial Distribution: Finding the number of trials

What is a binomial distribution?

A binomial distribution is a statistical probability distribution that describes the likelihood of a certain number of successes in a fixed number of independent trials, where the outcome of each trial can only result in a success or failure.

How do you calculate the number of trials in a binomial distribution?

To calculate the number of trials in a binomial distribution, you need to know the probability of success in each trial, denoted by p, and the number of successes you want to find, denoted by x. The formula is n = x/p, where n is the number of trials.

What is the difference between a binomial distribution and a normal distribution?

The main difference between a binomial distribution and a normal distribution is that a binomial distribution is discrete, meaning the outcome can only be a whole number, while a normal distribution is continuous, meaning the outcome can be any value within a certain range. Additionally, a binomial distribution is based on a fixed number of trials, while a normal distribution is based on a continuous range of values.

What is the significance of the binomial theorem in binomial distribution?

The binomial theorem is a mathematical formula that is used to expand binomials, which are expressions with two terms. In binomial distribution, the binomial theorem is used to calculate the probability of a certain number of successes in a fixed number of trials, given the probability of success in each trial. It is an essential tool in understanding and solving problems related to binomial distribution.

How is binomial distribution used in real life?

Binomial distribution is used in various fields such as biology, finance, and psychology to model and predict the probability of success or failure in a series of independent trials. It can be applied to situations such as predicting the success rate of a new drug, estimating the likelihood of a stock market crash, or understanding the probability of a person developing a certain disease based on their genetic makeup.

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