I must admit that I only learnt about the “multiple testing” problem in statistical inference when I started reading about A/B testing. In many ways I knew about it already, since the essence of it can be captured by a basic example in probability theory: suppose a particular event has a chance of 1% of happening. Now, if we make N attempts what is the probability that this event will have happened at least once among the N attempts?
This article started as an excuse to present a Python code that solves a one-dimensional diffusion equation using finite differences methods. I then realized that it did not make much sense to talk about this problem without giving more context so I finally opted for writing a longer article. I have divided this article into three posts, of which this is the first one.