Exercise

# Sample from normal distribution

**The normal distribution** is a frequent topic during interviews due to the vast applications of this distribution.

**A random sample** is a set of observed items from the whole population.
You can make inferences about the population based on a random sample taken from the population.
For example, you can calculate **the sample probability** which is an estimate of **the population's true probability**.

To compute **the sample probability**, calculate the proportion of the observations in a sample that meet the given criteria.

To compute **the true probability**, use probability functions.

Recall that:

- the
*standard*normal distribution has \(\mu = 0\) and \(\sigma^2 = 1\) (referred to as \(N(0, 1)\)), `pnorm(q = k)`

returns \(P(X \le k)\) for \(X \sim N(0, 1)\).

Instructions 1/2

**undefined XP**

- Generate 1000 data points from the
*standard*normal distribution. - Draw a histogram of the generated data points.