To construct a particular binomial probability, it is necessary to know the total number of trials and the probability of success on each trial. To calculate the variance, we subtract the mean from each value of x, square each of those differences, multiply by the probability of each x value, and sum the products.

## What is true for a binomial distribution?

A binomial distribution has only two possible outcomes on each trial, results from counting successes over a series of trials, the probability of success stays the same from trial to trial and successive trials are independent. You just studied 10 terms!

## What are the 4 conditions of a binomial setting?

1: The number of observations n is fixed. 2: Each observation is independent. 3: Each observation represents one of two outcomes (“success” or “failure”). 4: The probability of “success” p is the same for each outcome.

## Is the following situation a binomial setting?

Answer: This is a binomial setting.

## When can we use binomial distribution?

The binomial distribution model allows us to compute the probability of observing a specified number of “successes” when the process is repeated a specific number of times (e.g., in a set of patients) and the outcome for a given patient is either a success or a failure.

## What is the normal approximation to the binomial distribution?

Recall that if X is the binomial random variable, then X∼B(n,p). Then the binomial can be approximated by the normal distribution with mean μ=np and standard deviation σ=√npq. Remember that q=1−p. In order to get the best approximation, add 0.5 to x or subtract 0.5 from x (use x+0.5 or x−0.5).

## Is Bernoulli a normal distribution?

1 Normal Distribution. A Bernoulli trial is simple random experiment that ends in success or failure. A Bernoulli trial can be used to make a new random experiment by repeating the Bernoulli trial and recording the number of successes.