Consider a two digit number. With this algorithm, half of the numbers generated will be less than 10, and half will be 11-99.
Likewise with the full 100 digits this algorithm has a 1% chance of generating a number less than 10, while the correct proportion would be 1e-100.
The algorithm also does not generate numbers with zero in them (presumably to avoid leading zeroes, but this will skew results somewhat).
I think the correct algorithm would be to generate 100 digits and then remove the leading zeroes.
Semi-relevant: https://en.wikipedia.org/wiki/Benford%27s_law
Also relevant (and in my mind, the underlying explanation for Benford's Law):
Just getting into bayesian inference and this sent me down a rabbit hole, thanks :)
Damn...
People, I just ran this simple script on my terminal using Node and I got a pretty uniform result.
a = {}
for(let i = 0; i < 100000; i++){
let n = Math.floor(Math.random() * 10);
if( n in a )
a[n] += 1
else
a[n] = 1
}
{ '0': 9917, '1': 10015, '2': 9957, '3': 10107, '4': 10019, '5': 10037, '6': 10120, '7': 9914, '8': 10042, '9': 9872 }
> real-life sets of numerical data
Today you learned: Node’s random number generator isn’t selecting from a real-life set of numerical data
yeah, I just mixed two informations. I thought the other person said my algorithm would be biased towards small numbers cause I use the JS random function to pick the number of decimal places for the number
Yep, sorry, didn't mean to say this directly applied, just a neat concept that's somewhat related.
Why?