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Binomial distribution

The binomial distribution is a type of probability distribution that is commonly used to describe the outcome of a given number of independent and identically distributed Bernoulli trials. A Bernoulli trial is an experiment with only two possible outcomes, such as a coin toss or yes/no question.

### Uses of Binomial Distribution

The binomial distribution can be used for estimating the probability of observing a certain number of successes (the outcome of interest) within a certain number of trials. It is important to note that the binomial distribution can only be used when each trial has an equal chance of success and failure, which makes it ideal for situations such as experiments in genetics or epidemiology where certain conditions are tested against one another.

### Binomial Probabilities Calculation

In order to calculate binomial probabilities, you must first determine the total number of combinations (nCr) possible from n independent and equally likely events. For example, if you are trying to determine the probability of getting 3 heads out of 4 coin tosses, there would be 4 combinations possible: HHHH, HHHT, HTHH, THHH.

The formula for calculating this type of combination is

nCr= n! / r!(n-r)!

where n is the total number of events and r is the number desired from those events. This equation will yield 16 combinations (4! / 3!(4-3)!), so the probability would be 16/16 or 100%. In addition to determining probabilities based on combinations, the binomial distribution can also be used to calculate expected values for different scenarios. Expected values refer to the long-term average results obtained from performing a specific experiment multiple times under identical conditions. For instance, if you were interested in finding out how many heads would occur after tossing a fair coin 20 times, then you could use the binomial distribution equation

P(x) = nCr * p^x * q^(n-x)

where p = 0.5 (the probability associated with each event), x = 0 through 20 (the number desired), and q = 1 – p (the probability associated with not obtaining that event). Using this equation, you could calculate that after 20 flips there should be about 10 heads on average due to its 50/50 chance at being either heads or tails each time it’s flipped.