Exercise

# Samples from a rolled die

Let's work through generating a simulation using the `numpy`

package. You'll work with the same scenario from the slides, simulating rolls from a standard die numbered 1 through 6, using the `randint()`

function. Take a look at the documentation for this function if you haven't encountered it before.

Starting with a small sample and working your way up to a larger sample, **examine the outcome means** and come to a conclusion about the underlying theorem.

Instructions

**100 XP**

- Generate a sample of 10 die rolls using the
`randint()`

function; assign it to our`small`

variable. - Assign the mean of the sample to
`small_mean`

and print the results; notice how close it is to the true mean. - Similarly, create a larger sample of 1000 die rolls and assign the list to our
`large`

variable. - Assign the mean of the larger sample to
`large_mean`

and print the mean; which theorem is at work here?