The app I build can be used to demonstrate the central limit theorem.

My CLT app

The central limit theorem (CLT) establishes that, in some situations, when independent random variables are added, their properly normalized sum tends toward a normal distribution (informally a “bell curve”) even if the original variables themselves are not normally distributed.

For example, suppose that a sample is obtained containing many observations, each observation being randomly generated in a way that does not depend on the values of the other observations, and that the arithmetic mean of the observed values is computed. If this procedure is performed many times, the central limit theorem says that the distribution of the average will be closely approximated by a normal distribution.

Wikipedia

If I plot 100 values from an uniform distribution it looks like this:

It looks evenly distributed which we expect from an uniform distribution.

However, if I plot 100 averages it looks like this:

It is much closer to a normal distribution. The app can be used to explore this.