Simulating binary data

A Bernoulli distribution is a special case of a binomial. Next, you will see how to simulate both in R and then examine the outputs to see how they are similar. Both distributions can be simulated with the random binomial function: rbinom(). rbinom() requires 3 arguments:

n, which is the number of draws or random numbers (i.e., an output vector of length n).
size, which is the number of samples per draw (i.e., the maximum value for each random number).
prob, which is the probability for the simulation.

To sample with a Bernoulli, you simply use size = 1.

If we take a single random draw (n = 1) from a binomial distribution with a large number of samples per draw (e.g., size = 100), we should get similar results as a taking a many samples (e.g., n = 100) with 1 sample per draw (size = 1).

Simulate from a binomial distribution with 1 sample (n) and a size (size) of 100 and a probability (p) of 0.5.
Simulate from a Bernoulli distribution with 100 samples (n) and a size (size) of 1 and a probability (p) of 0.5.
Print the results from the first simulation.
Sum the results from the second simulation.

Take Hint (-30 XP)

Exercise

Simulating binary data

Instructions