I don't know about you, but I would bet that all of us have used a binomial random variable. I mean, how many of us have tested our luck with a coin toss or tried to anticipate whether or not the next person that walked through the door would be a certain gender or be performing some sort of action?

A binomial random variable can be easily described as "a count of how many times an event occurs (or does not occur) in a particular number of independent observations or trials that make up a random circumstance" ("Handout 4: Binomial Distribution", 2016). Ok, that's more of a definition, but you get the idea. As we learned in our weekly reading, the binomial random variable works alongside Bernoulli process, and although this all sounds complicated, as mentioned above, we have most likely used this process without realizing what it is.

For example, I'm a man of chance. I enjoy the thrill of randomness at times. Whether it be complicated or simple, I love trying to guess the outcome of a static event. I always keep a coin with me that has a yin symbol on one side and a yang symbol on the other. When I'm bored or waiting for something to happen, I'll flip the coin and try to count the outcome. It's not terribly exciting, but it kills the time and adds some entertainment. In this instance, the Bernoulli process is in effect. I don't do anything with the data, but sometimes I'll count the odds and see if they stay near each other. Just like yin and yang balance each out, I appreciate a balance of odds as well.

Handout 4: Binomial Distribution. (2016). Retrieved 30 August 2016, from http://www.stat.wmich.edu/s216/handouts/handout4.pdf