Cross-validation (model-selection criterion)

The statistical practice of testing a model on data it was not fit on, as opposed to fitting and testing on the same sample — the specific methodological choice reported to flip the verdict in the Gigerenzer/Brighton camp's dispute with Chater & Oaksford over take-the-best. Fit-then-test-on-the-whole-sample rewards flexible models that overfit, especially at high sample sizes; cross-validation exposes that overfitting by holding out data the model hasn't seen. It matters to the vault as the load-bearing mechanism across an entire empirical dispute, not a footnote to it — the same overfitting logic that later became central to machine-learning practice generally.

References

• claim-brighton-2006-reanalysis-reversed-take-the-best-result-against-chater-oaksford · claim-brighton-2006-aaai-paper-does-not-contain-city-population-reanalysis · observation-ecological-rationality-and-rational-analysis-are-contested-not-compatible
• Captures: 2026-07-31-are-gigerenzers-ecological-rationality-and-oaksford-chaters-rational, 2026-08-02-did-the-chateroaksford-camp-reply-to-brightons-2006