A very gentle introduction to e-values
Published:
Preliminaries
This blog post is intended to provide some intuition behind e-values, a framework for statistical hypothesis testing that has gained traction in the statistics and ML community over the last 5 years or so. The goal with this post is to briefly communicate what e-values are and how they’re connected to testing. I assume familiarity with statistics and the basic concepts of hypothesis testing, so be sure to brush up on those before reading. I will also use Markov’s inequality, which states that for any non-negative random variable $Z$ and $z>0$, \(P(Z \geq z) \leq \frac{\mathbb E[Z]}{z}.\)