PopFloor: Split-Half Diagnostics for Finite-Sample Wasserstein Bias in Single-Cell Perturbation Benchmarks

Single-cell perturbation models are evaluated by comparing predicted and measured populations. Yet the plug-in Wasserstein distance has a finite-sample floor and can fall when a prediction shrinks toward its mean, so underdispersed predictions can outscore a target sample. PopFloor estimates this split-half floor from measured cells. By an exact per-draw identity for the unregularised squared 2-Wasserstein distance, a spread-free population beats an equal-size target sample wherever the floor's ratio to the held-out variance exceeds one plus the relative mean error. In an exploratory recomputation, scPerturBench's leader falls to fourth without Wasserstein distance. Our reproduced CPA scores below the floor on 88.7 to 92.6% of the largest screen's perturbations in three seeds, and by the pre-registered rule beats it on three of five screens. The ratio and that share rise together from 10 to 5,000 genes under the exact estimator, as pre-registered, while its relative mean error falls. Pre-registered tests show the ratio's margin over that threshold predicts, beyond gene and cell counts, the preference on disjoint cells of two unused screens and, on the reproductions, entrants' advantage over an independent floor in 11 of 12 runs. PopFloor shows where Wasserstein rankings can reward underdispersion.