4.3 The Binomial Distribution
We have seen how to deal with general discrete random variables, but there are also special cases of DRVs. If we can identify them, they can provide us some insight and shortcuts. The first of these is the binomial distribution.
The Binomial Setting
There are three characteristics of a binomial experiment:
- There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
- There are only two possible outcomes for each trial: success and failure. The letter p denotes the probability of a success on one trial, and q denotes the probability of a failure on one trial. Note that p + q = 1.
- The n trials are independent and are repeated using identical conditions. Because they are independent, the outcome of one trial does not help in predicting the outcome of another trial. Another way of saying this is that, for each individual trial, the probability of a success (p) and probability of a failure (q) remain the same.
Let’s say that the withdrawal rate from an elementary physics course at ABC College is 30% for any given term. This implies that, for any given term, 70% of the students stay in the class for the entire term. A “success” could be defined as an individual who withdrew. In this instance, the random variable X represents the number of students who withdraw from the randomly selected elementary physics class.
Any experiment that has the second and third characteristics listed above and where n = 1 is called a Bernoulli trial (named after Jacob Bernoulli who studied them extensively in the late 1600s). A binomial experiment takes place when the number of successes is counted in one or more Bernoulli trials.
For example, randomly guessing at a true-false statistics question has only two outcomes. If a success is guessing correctly, then a failure is guessing incorrectly. If Joe always guesses correctly on any statistics true-false question with probability p = 0.6, then q = 0.4. This means that, for every true-false statistics question Joe answers, his probability of success (p = 0.6) and his probability of failure (q = 0.4) remain the same. This situation meets the binomial requirements.
In contrast, the following example illustrates a problem that is not binomial, as it violates the condition of independence. ABC College has a student advisory committee made up of ten staff members and six students. The committee wishes to choose a chairperson and a recorder. What is the probability that the chairperson and recorder are both students? The names of all committee members are put into a box, and two names are drawn without replacement. The first name drawn determines the chairperson, and the second name, the recorder. There are two trials. However, the trials are not independent because the outcome of the first trial affects the outcome of the second trial. The probability of a student on the first draw is . The probability of a student on the second d