In the next video, we will introduce the concept of binomial distribution.[br][br]Some of the formulas in the video are the ones that we would use if you didn't have your calculators. Luckily you do, so you won't need to use them almost ever.

The video presents some key elements about the binomial distribution, and we will work with them in class. But for now, we will keep them in hand.[br][br]A [b]binomial experiment[/b] is random experiment that has the following characteristics:[br][br][list=1][*][b]Fixed Number of Trials[/b]: The experiment consists of [i]n[/i] repeated trials.[/*][*][b]Binary Outcomes[/b]: Each trial results in one of two outcomes, often labeled as "success" and "failure". You can either get shocked by the toaster or not, a person could be a zombie or not, those are the two only possibilities.[/*][*][b]Constant Probability[/b]: The probability of success, denoted by [i]p[/i], is the same for each trial.[/*][*][b]Independence[/b]: The trials are independent, meaning the outcome of one trial does not affect the outcome of another.[/*][/list]In essence, a binomial experiment is a scenario in which you repeat a simple experiment multiple times, each time with the same conditions, and you are interested in the number of successes that occur. [br][br]If X is the number of successes in a binomial experiment with [i]n[/i] trials, each with probability of success [i]p[/i], then X is a [b]binomial random variable[/b].[br][br]The probability distribution of X is called [b]binomial distribution[/b], and we write [math]X\sim B\left(n,p\right)[/math].