Now that we’ve generated some random samples from a population using an applet, let’s code this ourselves in Python. The `numpy.random`

package has several functions that we could use to simulate random sampling. In this exercise, we’ll use the function `np.random.choice()`

, which generates a sample of some size from a given array.

In the example code, we’ll pretend that we’re all-powerful and actually have a list of all the weights of Atlantic Salmon that currently exist.

In the example code to the right, we have done the following:

- Loaded in the weights of all salmon into a dataframe called
`population`

. - Plotted the distribution of
`population`

and calculated the mean. - Used
`np.random.choice()`

function to generate a sample called`sample`

of size 30 (`samp_size`

variable is equal to`30`

).

### Instructions

**1.**

Find the mean of the `sample`

, round it to 3 decimal places, and assign it to a variable called `sample_mean`

.

**2.**

Uncomment the last 4 lines at the bottom of the editor to plot the histogram of the sample data.

*You might have to scroll down to see the 2nd plot. You can comment out the first plot’s plt.show() in order to avoid scrolling down each time.*

Run the code a couple of times. This code should behave similarly to the applet we used in the last exercise.

**3.**

Change the sample size to 10. Does the mean change more or less each time you run it with a smaller sample size?