The same linear-quadratic dose-response math as the 2D chart version drives this scene, but staged as two physical cell cultures instead of a curve: an exposed dish, dosed at the claimed exposure, and an unexposed control dish. Each of the 256 cells in a dish is an independent coin-flip against the model's predicted damage probability — watch individual cells actually die (shrink and redden) rather than reading a single aggregate percentage.
S(D) = e^(−αD − βD²)
per-cell: dies with probability p = 1 − S(D) (exposed)
p = background rate (control)
z = (p̂₁ − p̂₂) / sqrt(p̂·(1−p̂)·(1/n₁ + 1/n₂))
- Exposed dish (left) — each cell independently dies with the LQ model's predicted probability at the claimed dose; raise dose or radiosensitivity and watch more spheres shrink and turn red.
- Control dish (right) — cells die only at the background mutation rate, with zero radiation dose — this is the null-hypothesis baseline the exposed dish is judged against.
- Irradiate dishes — re-rolls both dishes from the current probabilities, and also redraws the full-sample z-test below so you can see how a small visual sample can look dramatic while the statistically rigorous test on the full assay says otherwise (or vice versa).
- Dish survival tally — the live fraction of the 256-cell visual sample still alive, shown alongside the model's exact predicted S(D) so you can see sampling noise directly on a finite dish.
This is the visual, cell-by-cell face of the same statistical test the 2D version runs numerically: real evidence of radiation-induced cellular damage requires the observed rate to clear the background noise floor by a statistically significant margin, not just look worse in a small dish.