One would use binomial distribution if and only if the experiment satisfies the following conditions
1. There is a fixed number of trials.
2. Each trial is independent of one another.
3. There are only two possible outcomes (a Success or a Failure).
4. The probability of success, p, is the same for every trial. An example of an experiment that has a binomial distribution would be a coin toss.
1. You would toss the coin a n (a fixed number) times.
2. The result of a a previous toss does not affect the present toss (trials are independent).
3. There are only two outcomes - Heads or Tails.
4. The probability of success (whether a head is considered a success or a tail is considered a success) is constant at 50%.
It is necessary to use a continuity correction when using a normal distribution to approximate a binomial distribution because the normal distribution contains real observations, while the binomial distribution contains integer observations.
First i will explain the binomial expansion
Binomial distribution is the basis for the binomial test of statistical significance. It is frequently used to model the number of successes in a sequence of yes or no experiments.
Poisson and Binomial both the distribution are used for defining discrete events.You can tell that Poisson distribution is a subset of Binomial distribution. Binomial is the most preliminary distribution to encounter probability and statistical problems. On the other hand when any event occurs with a fixed time interval and having a fixed average rate then it is Poisson distribution.
Two independent outcomes with constant probabilities.
It is necessary to use a continuity correction when using a normal distribution to approximate a binomial distribution because the normal distribution contains real observations, while the binomial distribution contains integer observations.
Use the continuity correction when using the normal distribution to approximate a binomial distribution to take into account the binomial is a discrete distribution and the normal distribution is continuous.
what are the uses of binomial distribution
what is meant by a negative binomial distribution what is meant by a negative binomial distribution
You distribute the binomial.
Binomial distribution is learned about in most statistic courses. You could use them in experiments when there are two possible outcomes and each experiment is independent.
In a symmetric binomial distribution, the probabilities of success and failure are equal, resulting in a symmetric shape of the distribution. In a skewed binomial distribution, the probabilities of success and failure are not equal, leading to an asymmetric shape where the distribution is stretched towards one side.
Normal distribution is the continuous probability distribution defined by the probability density function. While the binomial distribution is discrete.
First i will explain the binomial expansion
Yes, and the justification comes from the Central Limit Theorem.
Binomial distribution is the basis for the binomial test of statistical significance. It is frequently used to model the number of successes in a sequence of yes or no experiments.
Poisson and Binomial both the distribution are used for defining discrete events.You can tell that Poisson distribution is a subset of Binomial distribution. Binomial is the most preliminary distribution to encounter probability and statistical problems. On the other hand when any event occurs with a fixed time interval and having a fixed average rate then it is Poisson distribution.