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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%.
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Why is it necessary to use a continuity correction when using a normal distribution to approximate a binomial distribution?
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.
What is the relationship between the binomial expansion and binomial distribution?
First i will explain the binomial expansion
Why is binomial distribution important?
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.
What is the difference between poisson and binomial distribution?
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.
What are the conditions under which you can expect to be able to use a binomial distribution to model a probability distribution?
Two independent outcomes with constant probabilities.
