l2-epistemology_negative-empiricism

Falsification testing asks what single observation would prove your position wrong, rather than collecting confirming evidence. Define the refuting scenario upfront; if a theory cannot specify what would refute it, treat it as non-serious.

Negative empiricism is the principle that disconfirmation is stronger than confirmation: one counterexample destroys a theory while thousands of confirmations never prove it. Knowledge advances by eliminating what is false, not accumulating what seems true.

It uses data aggressively to attack and find opportunities but never to defend a position or prove safety. Historical patterns cannot confirm you are right; they only fail to have falsified you yet, as in the turkey problem.

Avoid relying on confirming evidence when survivorship bias or narrative fallacy may be inflating it, such as evaluating fund managers, scientific claims, or trading strategies. Silent evidence systematically hides failures from the dataset.

Induction fails precisely at the moment of maximum confidence, as the turkey's thousand days of feeding precede its slaughter. Forecasts built on accumulated confirmation carry hidden fragility that only disconfirmation reveals.