
Binomial Experiment
A binomial experiment is an experiment that has the following properties:
- The experiment consists of n repeated trials
- Each trial has only two possible outcomes: success or failure
- The probability of success, denoted p, is the same for each trial
- Each trial is independent; the outcome of one trial does not affect the outcome of another
Suppose we flip a coin three times and record the number of times the coin landed on heads. This is an example of a binomial experiment because:
- The experiment consists of 3 repeated flips
- Each flip has only two possible outcomes: heads or tails
- The probability of success (landing on heads) is 0.5 for each flip
- Each flip is independent; the outcome of one flip does not affect the outcome of another
Binomial Random Variable
A binomial random variable is the number of successes, denoted x, in a binomial experiment with n repeated trials, and it follows a binomial distribution.
For example, if we flip a coin three times, the number of times it lands on heads has the following binomial distribution, where X = number of times it lands on heads and P(X) = probability associated with that outcome:
The binomial distribution has the following properties:
- The mean of the distribution is μ = n * p
- The variance of the distribution is σ2 = n * p * (1-p)
- The standard deviation of the distribution is σ = √σ2
To find the probability of exactly x successes in n trials, given the probability of success in a single trial is p, we can use the following formula:
P(exactly x successes) = nCk * pk * (1-p)n-k
Let’s walk through some examples to gain a better understanding of the binomial distribution.
Binomial Distribution Example Problems
Example 1: John makes 60% of his free-throw attempts. If he shoots 12 free throws, what is the probability that he makes exactly 10?
Step 1: Identify the number of trials (n), the number of successes we’re interested in (k), and the probability of success on each trial (p).
n = number of free-throw attempts = 12
k = number of successful free-throws we’re interested in = 10
p = probability of successful free-throw on each attempt = 0.6
Step 2: Plug these numbers into the binomial formula or a binomial calculator
Using the formula:
P(John makes exactly 10 free-throws) = nCk * pk * (1-p)n-k
P(John makes exactly 10 free-throws) = 12C10 * (.6)10 * (1-.6)12-10
P(John makes exactly 10 free-throws) = (66) * (.006) * (.16)
P(John makes exactly 10 free-throws) = 0.063
Using the calculator:
Plug the following numbers into the Binomial Distribution Calculator:
The probability that John makes exactly 10 free-throws is about 6.3%.
Example 2: A factory receives a shipment of nails once per month. On average, 2% of the nails are defective. If there are 500 nails per shipment, what is the mean and standard deviation of the number of defective nails per shipment?
Step 1: Use the formulas for the mean and standard deviation of a binomial distribution to answer this question.
Mean (μ)= n * p = 500 * 0.02 = 10
Variance (σ2)= n * p * (1-p) = 500 * 0.02 * (1-0.02) = 9.8
Standard deviation = √σ2 = √9.8 = 3.13
The mean number of defective nails per shipment is 10 nails and the standard deviation is 3.13 nails.
Example 3: Ando flips a fair coin 5 times. What is the probability that the coin lands on heads 3 times or more?
Step 1: Identify the number of trials (n), the number of successes we’re interested in (k), and the probability of success on each trial (p).
n = number of flips = 5
k = number of times the coin lands on heads = 3
p = probability that coin lands on heads in a given flip = 0.5
Step 2: Since we want to know the probability that the coin lands on heads 3 times or more, we need to find the probability that it lands on heads 3 times, 4 times, and 5 times, then add up these probabilities.
Using the formula:
P(exactly 3 heads) = 5C3 * (.5)3 * (1-.5)5-3 = 0.3125
P(exactly 4 heads) = 5C4 * (.5)4 * (1-.5)5-4 = 0.15625
P(exactly 5 heads) = 5C5 * (.5)5 * (1-.5)5-5 = 0.03125
P(3 heads or more) = P(3 heads) + P(4 heads) + P(5 heads) = 0.5
Using the calculator:
Plug the following numbers into the Binomial Distribution Calculator:
The calculator tells us that the probability of getting 3 or more heads (PX ≥ x) is 0.5, which matches the result we got using the formula.