This is simply a test page for a random number generator that generates random numbers skewed to the normal distribution. From Wikipedia:
An easy to program approximate approach, that relies on the central limit theorem, is as follows: generate 12 uniform U(0,1) deviates, add them all up, and subtract 6 — the resulting random variable will have approximately standard normal distribution. In truth, the distribution will be Irwin—Hall, which is a 12-section eleventh-order polynomial approximation to the normal distribution. This random deviate will have a limited range of (-6, 6).
This tester will run the random normal number generator multiple (as input by the user) times. The resulting list of numbers is distributed across different decades of the span 0 to 1 and a bar graph is generated based on this data. The graphing is done via the plotly library.
The graph generated by plotly is interactive. Hover over a bar to get the number of random numbers it represents.
It is not clear how accurately this algorithm represents the normal distribution. Even after generating 10,000,000 random numbers there are no numbers in the 0 - 0.1 and 0.9 - 1 ranges. After 100,000,000 (~10 secs) there were 2 in 0.9 - 1 and 1 in 0 - 0.1.