Statistical properties of random samples have properties that are random at the sample level, but that are predictable for large collections of them. This is not always intuitive, because in real life we rarely have big enough collections to see it. With Python though, we can simulate these, and analyze them to see what is happening. We explore this by sampling the normal distribution. We illustrate the central limit theorem, which is if you make enough sample measurements, the distribution of the sample means is normally distributed. We further show that the standard deviation of that distribution is inversely proportional to the square root of the number of samples. While no substitute for analytical math, this style of simulation can be useful to build your intuition.
Learn more at https://pointbreezepubs.gumroad.com/l/pcyse-intermediate.
