The central limit theorem tells us that for a population with a mean $\mu$ and a finite variance $\sigma^2$, the sampling distribution of the sample means for a sample of size $n$ will be approximately normally distributed with a mean equal to $\mu$ and a variance equal to $\frac{\sigma^2}{n}$, no matter the distribution of the population, assuming a large sample size. When the standard deviation of the population is known, the standard error of the sample mean is:

$$\frac{\sigma}{\sqrt{n}} = \frac{0.5}{\sqrt{50}} = 0.0707$$

(Book 2, Module 17.1, LO 17.a)