= Solution
The <demarcation problem> concerns what distinguishes science from <pseudoscience>, not merely what distinguishes true claims from false ones. <Karl Popper>'s proposal is <falsifiability>: a scientific claim must exclude some possible observations, so that a test could in principle count against it. A theory that is compatible with every outcome gains no empirical credit simply by accommodating what occurred.<Science: Conjectures and Refutations by Karl Popper>[chapter 1 excerpt: incompatible observations and immunizing reinterpretations]{ref}
This captures an important methodological defect of <pseudoscience>: treating every apparent counterexample as further support. But bare <falsifiability> is not sufficient for good science. A sharply testable lucky-number prediction can be unsupported and methodologically irresponsible. Nor does a mistaken testable theory cease to have been scientific when it is rejected. Scientific status and evidential success are different assessments.
Necessity is also more complicated than a literal one-observation refutation rule. Statistical theories permit rare outcomes, and an instrument-dependent prediction tests <auxiliary hypotheses> as well as the main theory. Some current research proposals have no feasible immediate test, yet belong to programmes trying to derive discriminating consequences. Their status should depend partly on whether that programme produces such tests, rather than on a permanent exemption from empirical accountability.
Thus <falsifiability> supplies an important constraint on empirical content but not an adequate stand-alone classification of every scientific activity. Reliable <experiments>, openness to criticism, comparative explanatory success and correction of failed predictions also matter. This does not make demarcation pointless: it explains why an assessment of a practice over time can be more informative than attaching a binary label to one sentence.
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