An Equal-Probability Partition of the Sample Space: A Non-parametric Inference from Finite Samples

This paper investigates what can be inferred about an arbitrary continuous probability distribution from a finite sample of N observations drawn from it. The central finding is that the N sorted sample points partition the real line into N+1 segments, each carrying an expected probability mass of exactly 1/(N+1). This non-parametric result, which follows from fundamental properties of order statistics, holds regardless of the underlying distribution's shape. This equal-probability partition yields a discrete entropy of log2(N+1) bits, which quantifies the information gained from the sample and contrasts with Shannon's results for continuous variables. I compare this partition-based framework to the conventional ECDF and discuss its implications for robust non-parametric inference, particularly in density and tail estimation.