Stats-Beginner: to describe a binomial experiment that can solved w/ below

In summary, a binomial experiment is a statistical experiment with a fixed number of trials, two possible outcomes, and a constant probability of success for each trial. It can be described using the acronym "BINS" and examples include flipping a coin, rolling a die, and conducting surveys with yes or no questions. The probability of a binomial experiment can be calculated using a formula that takes into account the number of trials, successes, and probability of success. Binomial experiments are important in statistics as they allow for the analysis and prediction of data with two outcomes, and provide a foundation for more complex experiments and decision-making based on data.
  • #1
yamuna
4
0

Homework Statement



Describe a binomial experiment that can be solved using the expression
10C5 (0.2)5 (0.8)5

Homework Equations





The Attempt at a Solution



Have no idea.
 
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  • #2
Welcome to PF!

yamuna said:
Describe a binomial experiment that can be solved using the expression
10C5 (0.2)5 (0.8)5

Hi yamuna! Welcome to PF! :smile:

Hint: 0.2 + 0.8 = 1 … hmm :rolleyes: … so what might 0.2 be? :wink:
 
  • #3


I can help you understand how to approach this problem. A binomial experiment is a type of probability experiment that has two possible outcomes (success or failure) and a fixed number of trials. In this case, we have 10 trials. The expression 10C5 represents the combination of 10 trials taken 5 at a time. This means that we are interested in the probability of getting 5 successes out of 10 trials.

To further understand this, let's consider an example. Let's say we are conducting an experiment where we toss a coin 10 times and we are interested in the probability of getting 5 heads. In this case, the probability of getting a head is 0.2 and the probability of getting a tail is 0.8. So, the expression 10C5 (0.2)^5 (0.8)^5 represents the probability of getting 5 heads and 5 tails in any order out of 10 tosses.

In summary, the expression 10C5 (0.2)^5 (0.8)^5 represents a binomial experiment where we are interested in the probability of getting 5 successes (heads) out of 10 trials (coin tosses) with a probability of success (getting a head) of 0.2 and a probability of failure (getting a tail) of 0.8. I hope this helps you understand the concept better.
 

1. What is a binomial experiment?

A binomial experiment is a statistical experiment that has the following characteristics: it consists of a fixed number of trials, each trial has only two possible outcomes (success or failure), the probability of success remains the same for each trial, and the trials are independent of each other.

2. How can a binomial experiment be described?

A binomial experiment can be described by using the acronym "BINS", which stands for "Binary" (two possible outcomes), "Independent" (trials do not affect each other), "Number" (fixed number of trials), and "Success" (probability of success remains the same for each trial).

3. What are some examples of binomial experiments?

Examples of binomial experiments include flipping a coin, rolling a die, and conducting a survey with yes or no questions. These experiments all have two possible outcomes, a fixed number of trials, and the probability of success remains the same for each trial.

4. How do you calculate the probability of a binomial experiment?

The probability of a binomial experiment can be calculated using the formula: P(X=k) = nCk * p^k * (1-p)^(n-k), where n is the number of trials, k is the number of successes, and p is the probability of success for each trial. nCk is the combination formula, which calculates the number of ways k successes can occur in n trials.

5. What is the importance of binomial experiments in statistics?

Binomial experiments are important in statistics because they allow us to analyze and make predictions about data that has two possible outcomes. They also provide a framework for understanding more complex experiments and can be used to test hypotheses and make decisions based on data.

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