How it works
Falsification is rarely absolute in noisy data; evidence can update confidence rather than deliver one final verdict. Auxiliary assumptions also matter.
How to read it carefully
Write failure criteria and tolerated uncertainty before outcomes. Preserve disconfirming observations and distinguish a revised hypothesis as a new version.
