## Mean of Binomial Distribution

A binomial distribution represents the results from a simple experiment where there is “success” or “failure.” For example, if you are polling voters to see who is voting Democrat, the voters that say they will vote Democrat is a “success” and anything else is a failure. One of the simplest binomial experiments you can perform is a coin toss, where “heads” could equal “success” and “tails” could equal “failure.”

The **mean of binomial distribution** is much like the mean (i.e. the average) of anything else. It answers the question “If you perform this experiment many times, what’s the likely (the average) result?.

## Formula for Mean of Binomial Distribution

The formula for the **mean of binomial distribution** is:

**μ = n *p**

Where “n” is the number of trials and “p” is the probability of success.

For example: if you tossed a coin 10 times to see how many heads come up, your probability is .5 (i.e. you have a 50 percent chance of getting a heads and 50 percent chance of a tails) and “n” is how many trials — 10. Therefore, the mean of this particular binomial distribution is:

10 * .5 = 5.

This makes sense: if you toss a coin ten times you would expect heads to show up on average, 5 times.

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